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Our team has finished 9th in the third Data Science Bowl competition on Kaggle. Read about our approach here.


          Diagnosing Heart Diseases with Deep Neural Networks      Cache   Translate Page      

The Second National Data Science Bowl, a data science competition where the goal was to automatically determine cardiac volumes from MRI scans, has just ended. We participated with a team of 4 members from Ghent University and finished 2nd!

The team kunsthart (artificial heart in English) consisted of Ira Korshunova, Jeroen Burms, Jonas Degrave, 3 PhD students, and professor Joni Dambre. It’s also a follow-up of last year’s team ≋ Deep Sea ≋, which finished in first place for the First National Data Science Bowl.

Overview

This blog post is going to be long, here is a clickable overview of different sections.

Introduction

The problem

The goal of this year’s Data Science Bowl was to estimate minimum (end-systolic) and maximum (end-diastolic) volumes of the left ventricle from a set of MRI-images taken over one heartbeat. These volumes are used by practitioners to compute an ejection fraction: fraction of outbound blood pumped from the heart with each heartbeat. This measurement can predict a wide range of cardiac problems. For a skilled cardiologist analysis of MRI scans can take up to 20 minutes, therefore, making this process automatic is obviously useful.

Unlike the previous Data Science Bowl, which had very clean and voluminous data set, this year’s competition required a lot more focus on dealing with inconsistencies in the way the very limited number of data points were gathered. As a result, most of our efforts went to trying out different ways to preprocess and combine the different data sources.

The data

The dataset consisted of over a thousand patients. For each patient, we were given a number of 30-frame MRI videos in the DICOM format, showing the heart during a single cardiac cycle (i.e. a single heartbeat). These videos were taken in different planes including the multiple short-axis views (SAX), a 2-chamber view (2Ch), and a 4-chamber view (4Ch). The SAX views, whose planes are perpendicular to the long axis of the left ventricle, form a series of slices that (ideally) cover the entire heart. The number of SAX slices ranged from 1 to 23. Typically, the region of interest (ROI) is only a small part of the entire image. Below you can find a few of SAX slices and Ch2, Ch4 views from one of the patients. Red circles on the SAX images indicate the ROI’s center (later we will explain how to find it), for Ch2 and Ch4 they specify the location of SAX slices projected on the corresponding view.

sax_5 sax_9 sax_10 sax_11 sax_12 sax_15
2Ch 4Ch

The DICOM files also contained a bunch of metadata. Some of the metadata fields, like PixelSpacing and ImageOrientationm were absolutely invaluable to us. The metadata also specified patient’s age and sex.

For each patient in the train set, two labels were provided: the systolic volume and the diastolic volume. From what we gathered (link), these were obtained by cardiologists by manually performing a segmentation on the SAX slices, and feeding these segmentations to a program that computes the minimal and maximal heart chamber volumes. The cardiologists didn’t use the 2Ch or 4Ch images to estimate the volumes, but for us they proved to be very useful.

Combining these multiple data sources can be difficult, however for us dealing with inconsistencies in the data was more challenging. Some examples: the 4Ch slice not being provided for some patients, one patient with less than 30 frames per MRI video, couple of patients with only a handful of SAX slices, patients with SAX slices taken in weird locations and orientations.

The evaluation

Given a patient’s data, we were asked to output a cumulative distribution function over the volume, ranging from 0 to 599 mL, for both systole and diastole. The models were scored by a Continuous Ranked Probability Score (CRPS) error metric, which computes the average squared distance between the predicted CDF and a Heaviside step function representing the real volume.

An additional interesting novelty of this competition was the two stage process. In the first stage, we were given a training set of 500 patients with a public test set of 200 patients. In the final week we were required to submit our model and afterwards the organizers released the test data of 440 patients and labels for 200 patients from the public test set. We think the goal was to compensate for the small dataset and prevent people from optimizing against the test set through visual inspection of every part of their algorithm. Hand-labeling in the first stage was allowed on the training dataset only, for the second stage it was also allowed for 200 validation patients.

The solution: traditional image processing, convnets, and dealing with outliers

In our solution, we combined traditional image processing approaches, which find the region of interest (ROI) in each slice, with convolutional neural networks, which perform the mapping from the extracted image patches to the predicted volumes. Given the very limited number of training samples, we tried combat overfitting by restricting our models to combine the different data sources in predefined ways, as opposed to having them learn how to do the aggregation. Unlike many other contestants, we performed no hand-labelling .

Pre-processing and data augmentation

The provided images have varying sizes and resolutions, and do not only show the heart, but the entire torso of the patient. Our preprocessing pipeline made the images ready to be fed to a convolutional network by going through the following steps:

  • applying a zoom factor such that all images have the same resolution in millimeters
  • finding the region of interest and extracting a patch centered around it
  • data augmentation
  • contrast normalization

To find the correct zooming factor, we made use of the PixelSpacing metadata field, which specifies the image resolution. Further we will explain our approach to ROI detection and data augmentation.

Detecting the Region Of Interest through image segmentation techniques

We used classical computer vision techniques to find the left ventricle in the SAX slices. For each patient, the center and width of the ROI were determined by combining the information of all the SAX slices provided. The figure below shows an example of the result.

ROI extraction steps
ROI extraction steps

First, as was suggested in the Fourier based tutorial, we exploit the fact that each slice sequence captures one heartbeat and use Fourier analyses to extract an image that captures the maximal activity at the corresponding heartbeat frequency (same figure, second image).

From these Fourier images, we then extracted the center of the left ventricle by combining the Hough circle transform with a custom kernel-based majority voting approach across all SAX slices. First, for each fourier image (resulting from a single sax slice), the highest scoring Hough circles for a range of radii were found, and from all of those, the highest scoring ones were retained. , and the range of radii are metaparameters that severely affect the robustness of the ROI detected and were optimised manually. The third image in the figure shows an example of the best circles for one slice.

Finally, a ‘likelihood surface’ (rightmost image in figure above) was obtained by combining the centers and scores of the selected circles for all slices. Each circle center was used as the center for a Gaussian kernel, which was scaled with the circle score, and all these kernels were added. The maximum across this surface was selected as the center of the ROI. The width and height of the bounding box of all circles with centers within a maximal distance (another hyperparameter) of the ROI center were used as bounds for the ROI or to create an ellipsoidal mask as shown in the figure.

Given these ROIs in the SAX slices, we were able to find the ROIs in the 2Ch and 4Ch slices by projecting the SAX ROI centers onto the 2Ch and 4Ch planes.

Data augmentation

As always when using convnets on a problem with few training examples, we used tons of data augmentation. Some special precautions were needed, since we had to preserve the surface area. In terms of affine transformations, this means that only skewing, rotation and translation was allowed. We also added zooming, but we had to correct our volume labels when doing so! This helped to make the distirbution of labels more diverse.

Another augmentation here came in the form of shifting the images over the time axis. While systole was often found in the beginning of a sequence, this was not always the case. Augmenting this, by rolling the image tensor over the time axis, made the resulting model more robust against this noise in the dataset, while providing even more augmentation of our data.

Data augmentation was applied during the training phase to increase the number of training examples. We also applied the augmentations during the testing phase, and averaged predictions across the augmented versions of the same data sample.

Network architectures

We used convolutional neural networks to learn a mapping from the extracted image patches to systolic and diastolic volumes. During the competition, we played around a lot with both minor and major architectural changes. Our base architecture for most of our models was based on VGG-16.

As we already mentioned, we trained different models which can deal with different kinds of patients. There are roughly four different kinds of models we trained: single slice models, patient models, 2Ch models and 4Ch models.

Single slice models

Single slice models are models that take a single SAX slice as an input, and try to predict the systolic and diastolic volumes directly from it. The 30 frames were fed to the network as 30 different input channels. The systolic and diastolic networks shared the convolutional layers, but the dense layers were separated. The output of the network could be either a 600-way softmax (followed by a cumulative sum), or the mean and standard deviation of a Gaussian (followed by a layer computing the cdf of the Gaussian).

Although these models obviously have too little information to make a decent volume estimation, they benefitted hugely from test-time augmentation (TTA). During TTA, the model gets slices with different augmentations, and the outputs are averaged across augmenations and slices for each patient. Although this way of aggregating over SAX slices is suboptimal, it proved to be very robust to the relative positioning of the SAX slices, and is as such applicable to all patients.

Our single best single slice model achieved a local validation score of 0.0157 (after TTA), which was a reliable estimate for the public leaderboard score for these models. The approximate architecture of the slice models is shown on the following figure.

2Ch and 4Ch models

These models have a much more global view on the left ventricle of the heart than single SAX slice models. The 2Ch models also have the advantage of being applicable to every patient. Not every patient had a 4Ch slice. We used the same VGG-inspired architecture for these models. Individually, they achieved a similar validation score (0.0156) as was achieved by averaging over multiple sax slices. By ensembling only single slice, 2Ch and 4Ch models, we were able to achieve a score of 0.0131 on the public leaderboard.

Patient models

As opposed to single slice models, patient models try to make predictions based on the entire stack of (up to 25) SAX slices. In our first approaches to these models, we tried to process each slice separately using a VGG-like single slice network, followed by feeding the results to an overarching RNN in an ordered fashion. However, these models tended to overfit badly. Our solution to this problem consists of a clever way to merge predictions from multiple slices. Instead of having the network learn how to compute the volume based on the results of the individual slices, we designed a layer which combines the areas of consecutive cross-sections of the heart using a truncated cone approximation.

Basically, the slice models have to estimate the area (and standard deviation thereof) of the cross-section of the heart in a given slice . For each pair of consecutive slices and , we estimate the volume of the heart between them as , where is the distance between the slices. The total volume is then given by .

Ordering the SAX slices and finding the distance between them was achieved through looking at the SliceLocation metadata fields, but this field was not very reliable in finding the distance between slices, neither was the SliceThickness. We looked for the two slices that were furthest apart, drew a line between them, and projected every other slice onto this line. This way, we estimated the distance between two slices ourselves.

Our best single model achieved a local validation score of 0.0105 using this approach. This was no longer a good leaderboard estimation, since our local validation set contained relatively few outliers compared to the public leaderboard in the first round. The model had the following architecture:

Layer Type Size Output shape
Input layer   (8, 25, 30, 64, 64)*
Convolution 128 filters of 3x3 (8, 25, 128, 64, 64)
Convolution 128 filters of 3x3 (8, 25, 128, 64, 64)
Max pooling   (8, 25, 128, 32, 32)
Convolution 128 filters of 3x3 (8, 25, 128, 32, 32)
Convolution 128 filters of 3x3 (8, 25, 128, 32, 32)
Max pooling   (8, 25, 128, 16, 16)
Convolution 256 filters of 3x3 (8, 25, 256, 16, 16)
Convolution 256 filters of 3x3 (8, 25, 256, 16, 16)
Convolution 256 filters of 3x3 (8, 25, 256, 16, 16)
Max pooling   (8, 25, 256, 8, 8)
Convolution 512 filters of 3x3 (8, 25, 512, 8, 8)
Convolution 512 filters of 3x3 (8, 25, 512, 8, 8)
Convolution 512 filters of 3x3 (8, 25, 512, 8, 8)
Max pooling   (8, 25, 512, 4, 4)
Convolution 512 filters of 3x3 (8, 25, 512, 4, 4)
Convolution 512 filters of 3x3 (8, 25, 512, 4, 4)
Convolution 512 filters of 3x3 (8, 25, 512, 4, 4)
Max pooling   (8, 25, 512, 2, 2)
Fully connected (S/D) 1024 units (8, 25, 1024)
Fully connected (S/D) 1024 units (8, 25, 1024)
Fully connected (S/D) 2 units (mu and sigma) (8, 25, 2)
Volume estimation (S/D)   (8, 2)
Gaussian CDF (S/D)   (8, 600)

* The first dimension is the batch size, i.e. the number of patients, the second dimension is the number of slices. If a patient had fewer slices, we padded the input and omitted the extra slices in the volume estimation.

Oftentimes, we did not train patient models from scratch. We found that initializing patient models with single slice models helps against overfitting, and severely reduces training time of the patient model.

The architecture we described above was one of the best for us. To diversify our models, some of the good things we tried include:

  • processing each frame separately, and taking the minimum and maximum at some point in the network to compute systole and diastole
  • sharing some of the dense layers between the systole and diastole networks as well
  • using discs to approximate the volume, instead of truncated cones
  • cyclic rolling layers
  • leaky RELUs
  • maxout units

One downside of the patient model approach was that these models assume that SAX slices nicely range from one end of the heart to the other. This was trivially not true for patients with very few (< 5) slices, but it was harder to detect automatically for some other outlier cases as in figure below, where something is wrong with the images or the ROI algorithm fails.

sax_12 sax_15 sax_17 sax_36 sax_37 sax_41
2Ch 4Ch

Training and ensembling

Error function. At the start of the competition, we experimented with various error functions, but we found optimising CRPS directly to work best.

Training algorithm. To train the parameters of our models, we used the Adam update rule (Kingma and Ba).

Initialization. We initialised all filters and dense layers orthogonally (Saxe et al.). Biases were initialized to small positive values to have more gradients at the lower layer in the beginning of the optimization. At the Gaussian output layers, we initialized the biases for mu and sigma such that initial predictions of the untrained network would fall in a sensible range.

Regularization. Since we had a low number of patients, we needed considerable regularization to prevent our models from overfitting. Our main approach was to augment the data and to add a considerable amount of dropout.

Validation

Since the trainset was already quite small, we kept the validation set small as well (83 patients). Despite this, our validation score remained pretty close to the leaderboard score. Also, in cases where it didn’t, it helped us identify issues in our models, namely problematic cases in the test set which were not represented in our validation set. We noticed for instance that quite some of our patient models had problems with patients with too few SAX slices (< 5).

Selectively train and predict

By looking more closely at the validation scores, we observed that most of the accumulated error was obtained by wrongly predicting only a couple of such outlier cases. At some point, being able to handle only a handful of these meant the difference between a leaderboard score of 0.0148 and 0.0132!

To mitigate such issues, we set up our framework such that each individual model could choose not to train on or predict a certain patient. For instance, models on patients’ SAX slices could choose not to predict patients with too few SAX slices, models which use the 4Ch slice would not predict for patients who don’t have this slice. We extended this idea further by developing expert models, which only trained and predicted for patients with either a small or a big heart (as determined by the ROI detection step). Further down the pipeline, our ensembling scripts would then take these non-predictions into account.

Ensembling and dealing with outliers

We ended up creating about 250 models throughout the competition. However, we knew that some of these models were not very robust to certain outliers or patients whose ROI we could not accurately detect. We came up with two different ensembling strategies that would deal with these kind of issues.

Our first ensembling technique followed the following steps:

  1. For each patient, we select the best way to average over the test time augmentations. Slice models often preferred a geometric averaging of distributions, whereas in general arithmetic averaging worked better for patient models.
  2. We average over the models by calculating each prediction’s KL-divergence from the average distribution, and the cross entropy of each single sample of the distribution. This means that models which are further away from the average distribution get more weight (since they are more certain). It also means samples of the distribution closer to the median-value of 0.5 get more weight. Each model also receives a model-specific weight, which is determined by optimizing these weights over the validation set.
  3. Since not all models predict all patients, it is possible for a model in the ensemble to not predict a certain patient. In this case, a new ensemble without these models is optimized, especially for this single patient. The method to do this is described in step 2.
  4. This ensemble is then used on every patient on the test-set. However, when a certain model’s average prediction disagrees too much with the average prediction of all models, the model is thrown out of the ensemble, and a new ensemble is optimized for this patient, as described in step 2. This meant that about ~75% of all patients received a new, ‘personalized’ ensemble.

Our second way of ensembling involves comparing an ensemble that is suboptimal, but robust to outliers, to an ensemble that is not robust to them. This approach is especially interesting, since it does not need a validation set to predict the test patients. It follows the following steps:

  1. Again, for each patient, we select the best way to average over the test time augmentations again.
  2. We combine the models by using a weighted average on the predictions, with the weights summing to one. These weights are determined by optimising them on the validation set. In case not all models provide a prediction for a certain patient, it is dropped for that patient and the weights of the other models are rescaled such that they again sum to one. This ensemble is not robust to outliers, since it contains patient models.
  3. We combine all 2Ch, 4Ch and slice models in a similar fashion. This ensemble is robust to outliers, but only contains less accurate models.
  4. We detect outliers by finding the patients where the two ensembles disagree the most. We measure disagreement using CRPS. If the CRPS exceeds a certain threshold for a patient, we assume it to be an outlier. We chose this threshold to be 0.02.
  5. We retrain the weights for the first ensemble, but omit the outliers from the validation set. We choose this ensemble to generate predictions for most of the patients, but choose the robust ensemble for the outliers.

Following this approach, we detected three outliers in the test set during phase one of the competition. Closer inspection revealed that for all of them either our ROI detection failed, or the SAX slices were not nicely distributed across the heart. Both ways of ensembling achieved similar scores on the public leaderboard. (0.0110)

Second round submissions

For the second round of the competition, we were allowed to retrain our models on the new labels (+ 200 patients). We were also allowed to plan two submissions. Of course, it was impossible to retrain all of our models during this single week. For this reason, we chose to only train our 44 best models, according to our ensembling scripts.

For our first submission, we splitted of a new validation set. The resulting models were combined using our first ensembling strategy.

For our second submission, we trained our models on the entire training set (i.e. there was no validation split). We assembled them using the second ensembling method. Since we had no validation set to optimise the weights of the ensemble, we computed the weights by training an ensemble on the models we trained with a validation split, and transferred them over.

Software and hardware

We used Lasagne, Python, Numpy and Theano to implement our solution, in combination with the cuDNN library. We also used PyCUDA for a few custom kernels. We made use of scikit-image for pre-processing and augmentation.

We trained our models on the NVIDIA GPUs that we have in the lab, which include GTX TITAN X, GTX 980, GTX 680 and Tesla K40 cards. We would like to thank Frederick Godin and Elias Vansteenkiste for lending us a few extra GPUs in the last week of the competition.

Conclusion

In this competition, we tried out different ways to preprocess data and combine information from different data sources, and thus, we learned a lot in this aspect. However, we feel that there is still a room for improvement. For example, we observed that most of our error still hails from a select group of patients. These include the ones for which our ROI extraction fails. In hindsight, hand-labeling the training data and training a network to do the ROI extraction would be a better approach, but we wanted to sidestep doing a lot of this kind of manual effort as much as possible. In the end, labeling the data would probably have been less time intensive.

UPDATE (March 23): the code is now available on GitHub: github.com/317070/kaggle-heart


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Our team from Ghent University consisting of Sander, Aaron, Jeroen, Lionel, Pieter, Jonas and myself has won the National Data Science Bowl competition on Kaggle!

Read our description in Sander’s amazing blog post.


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NY-New York, ROLE SUMMARY BA’s Data Science team shapes state-of-the-art new analytic capabilities to drive business transformation. The Data Science Customer Analytics role is a subject matter expert working to guide the evolution of Pfizer’s commercial model globally through the application of advanced analytics to guide customer strategy for healthcare professionals (HCPs). This role is responsible to guide
          The Test Facing Democratic Socialist Julia Salazar in New York      Cache   Translate Page      

For decades, the New York City Democratic machine has capitalized off of low-turnout elections to keep its members in power. However, a new generation of progressive New Yorkers is rejecting that model, arguing that mobilizing new voters can drive electoral success. As Alexandria Ocasio-Cortez’s shocking victory in June highlighted, this “mobilize the base” strategy can upend the political establishment.

Like Ocasio-Cortez, Julia Salazar, a 27-year-old state senate candidate, is mounting a volunteer-driven challenge to a long-time incumbent, running as an open democratic socialist and rejecting corporate money. In a city that has been confronted with a systemic housing crisis and mass displacement, many candidates from the Left see machine-affiliated incumbents as complacent and vulnerable to populist criticisms. Salazar is no different. At the heart of her campaign is a critique of her opponent’s proximity to New York real estate interests and his role in propelling a rent crisis in the district. Rent stabilization laws in New York State are up for renewal in 2019, making this a decisive election year. Among Salazar’s chief platform planks are ending vacancy decontrol and instituting universal rent stabilization.

Salazar’s opponent is 16-year incumbent Martin Dilan in New York’s 18th Senate District, which includes parts of Greenpoint, Williamsburg, Bushwick and Cypress Hill. Dilan is one of the last holdouts of the political machine of Vito Lopez, the now deceased former Brooklyn Democratic Party Chair. Lopez was a state assemblyman for 30 years. During his tenure, he turned his nonprofit, the Ridgewood Bushwick Senior Citizens Council, into a formidable political operation. At its height in 2010, the RBSCC held $100 million in city and state contracts and engaged in classic quid-pro-quo politics. Lopez resigned as Brooklyn Democratic Chair and Assemblyman in 2013, after facing highly credible sexual harassment allegations. Dilan and his son, Councilman Erik Dilan, were among the largest beneficiaries of Lopez’s machine, funneling taxpayer money to RBSCC in exchange for political support.

But Lopez’s machine is no more, and there are strong reasons to see strength behind Salazar’s challenge. In 2014, and again in 2016, activist Debbie Medina mounted spirited challenges against Dilan’s proximity to real-estate interests and his failure to effectively respond to skyrocketing rents. However, the summer before the September 2016 primary, child abuse allegations surfaced against Medina. Despite losing critical endorsements and suffering from a crippling deficit of volunteers, Medina still won nearly 41 percent of the vote.

Clearly, voters in the 18th District are open to change. The question is whether Julia Salazar can capitalize on it.

Past under scrutiny

Salazar’s personal biography has come under scrutiny in recent weeks. Her identity as an immigrant, her assertions of growing up working-class and her Judaism have all been questioned, revealing a complex series of accounts from the candidate that could be characterized as everything from deliberately misleading statements to forgivable lapses of judgment. It was additionally revealed that she was previously a leader in right-wing activism at Columbia University, and that she was questioned over impersonating a family friend to access their financial information. As with many questions of personal identity, the story is not entirely black and white.

While Salazar’s website referred to the candidate as a “proud immigrant,” in August it was revealed that she was actually born in Miami, Fla., though she traveled back and forth between the United States and Colombia as a child. Salazar says she “never intended to misrepresent my personal narrative or my immigration status,” and that while several outlets, and even she herself, had made reference to her immigrant status, her statements were the product of imprecise early childhood memories and a strong identification with Colombia and her father’s family. She expressed regret that she hadn’t more thoroughly interrogated her memories as she began to run for public office, saying that “when I set out to run for State Senate I wasn’t critically thinking about my biography, specifically what has been challenged in my early childhood.”

Her early childhood has come under scrutiny from another angle: her class status. She has positioned herself as coming from a working-class background. The first time we talked she described how her economic conditions are foundational to her politics. However, her father worked as a pilot, and her brother has strongly contested her narrative of a working-class upbringing. Salazar admits that her early childhood, until age six, when her parents separated, was not one of economic uncertainty. However, she told me that after the separation, her father paid child support inconsistently at best, and the family relied on Social Security once he became disabled. She said that her mother had to return to college and made $17,000 dollars in one year while raising two children, and that her family’s “economic status varied greatly throughout my upbringing. The background that my parents came from, paired with the financial experience I had in elementary school or middle school are very safe to describe as working class.”

She says that “the lines aren’t fixed when it comes to class status. But when I talk about having working-class or middle-class experience, I mean that I understand the financial insecurity that people in my district face.” She says that “it’s not helpful or meaningful to debate whether I was poor,” but rather that her campaign is “about building solidarity with people who have to work for a living with families that know that if someone gets sick they could lose their home.” Salazar claims that class isn’t a rigid identity, but that it varies with economic status, and the combination of financial precarity and security in her upbringing complicates discussions of her class.

Ultimately, she believes that “voters are more concerned with my record as an advocate and my ability to fight for constituents.”

Which is why her history as a right-wing college activist bears addressing. After first registering as a Republican in Florida in 2008, Salazar says that after moving to New York she intended to register as an Independent but instead accidentally registering for the state’s Independence Party.

At Columbia University, she was involved with both pro-Israel and pro-life activism. Columbia is where she embraced the Jewish faith, which she says was spurred on by the death of her father, who “had made allusions to a Sephardic surname.” Her integration in Jewish life at Columbia, through organizations like Challah for Hunger and Hillel, encouraged her to visit Israel. The details of her trip have also been a source of controversy about her Jewish identity, as the trip was planned by Christians United for Israel rather than a Jewish organization. Nevertheless, she cites her visit to Israel, in 2012, and seeing the separation barrier particularly, as disabusing her of her firm pro-Israel stance. Being “very disturbed by the violence I saw there,” she says she made “a significant decision to reject pro-Israel advocacy.”

She described her experience in pro-life activism, with Columbia Right to Life, as echoing her other right-wing commitments. Her allegiance was shifted in the controversy around the creation of a Columbia abortion fund, which every student would pay into as part of their university-provided health plan. Salazar led the Support Pregnant Students Initiative, aiming to provide support for students who choose to keep their pregnancy. After she published an op-ed in the Columbia Spectator arguing her position, she was confronted by a number of close friends who, through a series of “very hard conversations,” led her to “realize I was deeply miseducated and wrong about abortion.”

Moving left

When asked whys he hadn’t made her political evolution part of her campaign story, Salazar says she was more focused on the community that had encouraged her to run for office, and that she “didn’t think about my own narrative so much and my own evolution.”

As an outgrowth of her political evolution, Salazar helped organize a rent strike against an abusive and absent landlord during her junior year at Columbia. She and her fellow tenants took their landlord to court and won. Her victory, though, was fleeting: when her lease ended a few months later, her landlord declined to renew it. Accordingly, the event holds two meanings for her: while “it was empowering to see how people without law degrees, without any institutional power were able to fight management and win,” she “was still being displaced. It was a very vivid example of the need to fight for systemic changes to systemic problems.”

Salazar says that experience helped propel her run for office.

When we spoke the first time, Salazar expressed how she was “tired of having to ask the same elected officials over and over again to do the right thing.” Her work in Albany and New York City, along with the support of her community and fellow organizers, convinced her that the solution wasn’t bird-dogging her representatives, but replacing them.

Changing landscape

Julia Salazar’s campaign is just one hint that change may be on the horizon in the New York State Senate. Members of the Independent Democratic Conference, (IDC) a group of Democratic senators who caucused with Republicans for the past eight years, are appearing increasingly weak in the face of strong primary challenges. And, if he wins reelection, Gov. Andrew Cuomo may find himself between a rock and a hard place come 2019.

According to Bill Lipton, the New York State Director of the Working Families Party, a progressive, or even a Democratic majority in the state senate is just what Cuomo has been trying to avoid. “Andrew Cuomo has spent the last eight years doing everything he can to keep progressives out of power in Albany—from allowing the Senate Republicans to gerrymander their own districts after promising repeatedly not to, to refusing to campaign for Senate Democrats, to helping the IDC steal the Democratic majority away in 2012, to fostering division between IDC and the Democratic caucus.” This arrangement has allowed Cuomo to act as the power broker in the state senate while both helping block progressive legislation and retaining his Democratic credentials.

Lipton claims Cuomo has utilized this arrangement to masquerade as a progressive while keeping legislative threats to his moneyed friends dead in the water, all the while joking to Senate Republicans about how little he supports Democratic candidates. That Cuomo has managed to build one of the largest campaign war chests in the country through corporate donations speaks to the immense wealth that this arrangement has granted. Lipton says real estate is among the chief interests that pay into this racket: “Real estate is to New York what coal is to West Virginia—the lifeblood of oligarchy.”

Housing crisis

It may be difficult for many residents of the 18th District to square their soaring rents with the $200,000 in real estate money Sen. Dilan has raised during his tenure in office, especially in a district ravaged by pro-real estate legislation and ensuing gentrification. Dilan’s vote has been instrumental in both implementing vacancy decontrol in 1994 as a city councilman, and defusing attempts to repeal the law in 2010 as a state senator. Vacancy decontrol, which allows landlords to freely raise the rent on vacant rent-stabilized units, is at the center of the city-wide housing crisis. According to Salazar, the 18th District alone loses tens of thousands of rent stabilized and controlled units every year.

And considering the state of New York City’s rent laws, which are set on the state level, that isn’t surprising. Once rent on an apartment reaches $2,733.75, landlords can “free” a unit from its rent-stabilized status and instead charge market rates.

There are two main ways that landlords jack up the rent: when a renovation is made owners are allowed a commensurate increase in rent, and the rent can be raised when a tenant moves out. In practice, both of these regulations produce perverse incentives. In the first case, landlords are given very little oversight when reporting the added value of a renovation, meaning they often make small improvements while disproportionately increasing rent. As for the second, landlords are incentivized to rent to short-term tenants, like college students and young people who don’t have ties to the neighborhood, and to harass their rent-stabilized tenants.

The scale of tenant abuse is staggering: A New York Times investigation found stories of landlords who punched holes in the roofs and walls of occupied rent-controlled units, bombarded tenants with eviction suits and harassed them with loud construction. The investigation discovered that the agencies responsible for regulation were “fractionalized, divided among three city and state agencies” and “essentially passive.” This regulatory environment has had devastating impacts on the 18th District. In Bushwick, a family living on the median yearly income, just over $42,000, would have to spend more than 60 percent of their income to afford the average rent of a two-bedroom unit. In Williamsburg, that number is 65 percent.

While Dilan argues that he never thought his vote would come to harm his own constituents, Jennifer Lenow, a member of NYC-DSA’s organizing committee for the Brooklyn Housing Working Group, says that Dilan’s “thousands and thousands from real estate speaks louder than his rhetoric.”

Salazar has been endorsed by the New York City chapter of the Democratic Socialists of America, U.S. Rep. Nydia Velazquez and City Council Member Antonio Reynoso, as well as Cynthia Nixon and Alexandria Ocasio-Cortez. Michael Kinnucan, Salazar’s Deputy Campaign Director, is optimistic about her chances, citing the broad dissatisfaction with the political status quo, the support of local activist groups Make the Road Action and New York Communities for Change, and “one of the largest volunteer ground-organizations in the city.”

Polling shows state-wide enthusiasm for large-scale investment in public housing, signaling that New Yorkers would look favorably upon a more proactive approach to confronting the state’s housing crisis.

As part of their New Progressive Agenda Project, Civis Analytics, a data analytics firm, and the think tank Data for Progress (where I am a fellow) have shown that 59 percent of likely 2018 voters in New York support billions of dollars in investment in public housing, even after hearing Republican arguments against it and being presented with an explicit tax pay-for through tax hikes. “These numbers are pretty consistent with the very high levels of support we’ve seen for public housing ballot measures in cities like Portland and Seattle,” says David Shor, head of data science at Civis Analytics’ political practice.

Persona and policy

Salazar’s controversies, meanwhile, have continued in recent days. On September 6, allegations of an affair with former New York Mets player Keith Hernandez and an attempt to impersonate his then-estranged, now divorced wife, Kai, have come to light. Salazar had been family friends with Hernandez, and was arrested for, but ultimately not charged with, the felony impersonation of Kai. According to Salazar’s statement, the lawsuit was entirely the product of a vengeful ex-wife who had gone so far as to impersonate Salazar on the phone in an attempt to frame her for illegally accessing her finances. Kai’s motive, according to Salazar, was that while house-sitting at Kai’s request Salazar had found “a lot of drugs, syringes, and several guns.” Salazar says that after calling Keith and describing the condition of the house, he, along with local police came to document the scene. The next year she was called by local police and interrogated for allegedly impersonating Kai. After not being charged, in 2013, Salazar sued Kai Hernandez for damages and the two parties reached a settlement in 2017.

When asked for comment about the controversies surrounding Salazar, Martin Dilan’s spokesman Bob Liff said that while “Marty has been a progressive and stable champion for residents of the 18th district, his opponent will have to speak for herself.”

A victory by Salazar’s insurgent campaign would offer even more proof that the future of the Democratic Party is multi-racial, decidedly left, community-driven and not beholden to corporate interests. However, the narrative surrounding her has become thick with controversy, misrepresentation and outright lies. While it is quite possible, as many of Salazar’s supporters claim, that the candidate has been victim to a right-wing smear campaign, there remain legitimate questions about how she has publicly presented herself throughout her campaign.

In a district whose neighborhoods are synonymous with gentrification, the question facing voters in the 18th is this: To what extent is representative politics about personality, and to what extent is it about policy? Thursday’s election will help provide an answer. 


          Python A-Z™: Python For Data Science With Real Exercises      Cache   Translate Page      
Python A-Z™: Python For Data Science With Real Exercises
Python A-Z�: Python For Data Science With Real Exercises
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 11 Hours | Lec: 69 | 2.18 GB
Genre: eLearning | Language: English

Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization

There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different!


          Advanced Data Scientist      Cache   Translate Page      
GA-Atlanta, Advanced Data Scientist Driving Infinite Possibilities Within A Diversified, Global Organization Honeywell is a Fortune 100 company with global sales surpassing $40B and has been one of Fortune's Most Admired Companies for over a decade. Through innovation, the Company brings together the physical and digital world to tackle some of the toughest societal and business problems - making the world a
          Sales Engineer - Hitachi Vantara - New York, NY      Cache   Translate Page      
Account Managers, internal specialists and customers. Understanding of Data Science and Machine Learning....
From Hitachi Vantara - Sat, 04 Aug 2018 04:47:47 GMT - View all New York, NY jobs
          Data Scientist - ThinkData - Toronto, ON      Cache   Translate Page      
Our platform, Namara, will propel the next wave of intelligent solutions. ThinkData Works Inc....
From ThinkData - Thu, 09 Aug 2018 00:36:56 GMT - View all Toronto, ON jobs
          Technology & Data Science Paired With Peer Coaching At New Addiction Center      Cache   Translate Page      
A new nonprofit addiction support center opened in Denver on Wednesday. The center is operated by the group Face It TOGETHER.
          CONSULTOR DATA SCIENCE BOGOTÁ - managementsolutions - Bogotá, Cundinamarca      Cache   Translate Page      
Recién titulados o estudiantes de último curso de Matemáticas, Físicas, Estadística, Econometría, Ingeniería u otros estudios con fuerte componente cuantitativo...
De managementsolutions - Sat, 21 Jul 2018 06:54:58 GMT - Ver todos: empleos en Bogotá, Cundinamarca
          Director – Customer Analytics, Data Science      Cache   Translate Page      
NY-New York, ROLE SUMMARY BA’s Data Science team shapes state-of-the-art new analytic capabilities to drive business transformation. The Data Science Customer Analytics role is a subject matter expert working to guide the evolution of Pfizer’s commercial model globally through the application of advanced analytics to guide customer strategy for healthcare professionals (HCPs). This role is responsible to guide
          Senior Data Scientist - Prudential - Newark, NJ      Cache   Translate Page      
Identify analytical solutions for business problems. And a passion for generating business impact. Develop and maintain consultative relationships with key...
From Prudential - Wed, 11 Jul 2018 21:51:46 GMT - View all Newark, NJ jobs
          Data Scientist - Prudential - Newark, NJ      Cache   Translate Page      
And a passion for generating business impact. Develop and maintain consultative relationships with key business stakeholders....
From Prudential - Wed, 11 Jul 2018 21:49:38 GMT - View all Newark, NJ jobs
          Senior Data Scientist Consultant - GI Research - Milano, Lombardia      Cache   Translate Page      
The successful candidate will be a valid partner of the Business Developer Data Services contributing and developing the current retail and supplier client...
Da GI Research - Thu, 23 Aug 2018 17:20:19 GMT - Visualizza tutte le offerte di lavoro a Milano, Lombardia
          Accountant      Cache   Translate Page      
CA-Redwood City, Accountant PCG Consulting is currently seeking an Accountant to join our team in Redwood City, CA 94063. Who we are: • Pacific Consulting Group (PCG) conducts research and statistical analysis for federal governmental and corporate clients. Since 1980 PCG has excelled at identifying insights and deriving meaning using data science techniques to inform policy and strategy decisions and improve prod
          Principal Market Validation Specialist - PTC - Needham, MA      Cache   Translate Page      
Advance knowledge and experience with Machine Learning / Data Science / Analytics. Customer Satisfaction focus, both internal and external, with strong...
From PTC - Wed, 16 May 2018 14:29:21 GMT - View all Needham, MA jobs
          Accountant      Cache   Translate Page      
CA-Redwood City, Accountant PCG Consulting is currently seeking an Accountant to join our team in Redwood City, CA 94063. Who we are: • Pacific Consulting Group (PCG) conducts research and statistical analysis for federal governmental and corporate clients. Since 1980 PCG has excelled at identifying insights and deriving meaning using data science techniques to inform policy and strategy decisions and improve prod
          Paramount Recruitment: Data Scientist Computational Biology      Cache   Translate Page      
Negotiable: Paramount Recruitment: Data Scientist Computational Biology Data Science, Omics, Bioinformatics, Pharmaceutical Competitive salary, generous relocation package, great work-life balance! A fantastic, challenging new opportunity has become available with a Top Pharmaceu Baden-Württemberg, Germany
          PRACTICAL SQL 2017 Online Training (Delhi)      Cache   Translate Page      
SQL School is one of the best training institutes for Microsoft SQL Server Developer Training, SQL DBA Training, MSBI Training, Power BI Training, Azure Training, Data Science Training, Python Training, Hadoop Training, Tableau Training, Machine Learning ...
          ALTERNANCE Assistant Business Intelligence / DATA H/F      Cache   Translate Page      
Entité de rattachement 1er groupe bi-média de France en audience print-digital, Prisma Media est aussi l'acteur N°1 en presse magazine et en audience vidéo. Un leadership qui assure à Prisma Media un potentiel optimal d'audience de plus de 40 millions de personnes chaque mois sur ses différents médias. Avec un portefeuille de 25 marques incontournables, le groupe est présent sur les principaux segments grand public (féminin, cuisine, télé, people, découverte, économie...). Porté par la mission de devancer les besoins et envies de ses lecteurs et utilisateurs sur tous les supports, Prisma Media adopte une stratégie offensive de développement et d'innovation dans les secteurs en forte croissance tels que la monétisation de la data, la vidéo et le mobile, avec une ambition d'avoir toujours UN MÉDIA D'AVANCE. Description de la mission Au sein de l'IT Entreprise, l'alternant sera rattaché à l'équipe BI/DATA qui est composée de 4 personnes. Ses principales missions autour de la BI et de la data science couvriront les domaines de la gestion des abonnés et la gestion des ventes de nos titres dans les points de ventes : - Réalisation de dashboards Business Objects et Qlikview - Réalisation de flux ETL et de procédures PL/SQL - Réalisation de data vizualisation - Participation à des POC de data science
          Data Scientist - FedEx Services - Brookfield, WI      Cache   Translate Page      
Women’s Business Enterprise National Council “America’s Top Corporations for Women’s Business Enterprises” - 2016....
From FedEx - Wed, 29 Aug 2018 00:08:19 GMT - View all Brookfield, WI jobs
          Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Knowledge and experience on applying statistical and machine learning techniques on real business data....
From Lincoln Financial Group - Mon, 27 Aug 2018 18:47:26 GMT - View all Boston, MA jobs
          Sr. Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Implements and maintains predictive and statistical models to identify business opportunities and solve complex business problems....
From Lincoln Financial Group - Fri, 17 Aug 2018 18:34:07 GMT - View all Boston, MA jobs
          Senior Data Scientist - Aerospace - Honeywell - Phoenix, AZ      Cache   Translate Page      
Drive the interaction with Honeywell internal business partners to define, design, and develop solutions to customer problems using data insights derived from...
From Honeywell - Wed, 12 Sep 2018 20:57:40 GMT - View all Phoenix, AZ jobs
          Data Scientist - Game Hive - Toronto, ON      Cache   Translate Page      
Our most popular games include the Tap Titans, Beat the Boss and Antrim Escape series (and many, more!). Game Hive is building a new generation of casual mobile...
From Indeed - Wed, 15 Aug 2018 13:34:49 GMT - View all Toronto, ON jobs
          Data Scientist ( Solutions Engineer Advisor Sr )      Cache   Translate Page      
GA-Atlanta, Description The Solutions Engineer Advisor Sr (Data Scientist) will be responsible for programming, oversight, and data extraction and development on specific application subsets of the company's application portfolio, participating in all phases of the development and maintenance life cycle, typically for an assigned business unit or corporate department and utilizing various technology platforms
          Data Science Manager - Micron - Boise, ID      Cache   Translate Page      
Create server based visualization applications that use machine learning and predictive analytic to bring new insights and solution to the business....
From Micron - Wed, 05 Sep 2018 11:18:49 GMT - View all Boise, ID jobs
          Intern - Data Scientist (NAND) - Micron - Boise, ID      Cache   Translate Page      
Machine learning and other advanced analytical methods. To ensure our software meets Micron's internal standards....
From Micron - Wed, 29 Aug 2018 20:54:50 GMT - View all Boise, ID jobs
          Intern - Data Scientist (DRAM) - Micron - Boise, ID      Cache   Translate Page      
Machine learning and other advanced analytical methods. To ensure our software meets Micron's internal standards....
From Micron - Mon, 20 Aug 2018 20:48:37 GMT - View all Boise, ID jobs
          Data Scientist - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Tue, 05 Jun 2018 16:15:49 GMT - View all New York, NY jobs
          Practical Data Science with R2      Cache   Translate Page      
The secret is out: Nina Zumel and I are busy working on Practical Data Science with R2, the second edition of our best selling book on learning data science using the R language. Our publisher, Manning, has a great slide deck describing the book (and a discount code!!!) here: We also just got back our … Continue reading Practical Data Science with R2
          Three UW teams receive TRIPODS+X grants for research in data science      Cache   Translate Page      
The National Science Foundation announced on Sept. 11 that it is awarding grants totaling $8.5 million to 19 collaborative projects at 23 universities for the study of complex and entrenched problems in data science. Three of these projects will be based at the University of Washington and led by researchers in the College of Engineering and the College of Arts & Sciences.
          Introducing Flint: A time-series library for Apache Spark      Cache   Translate Page      

This is a joint guest community blog by Li Jin at Two Sigma and Kevin Rasmussen at Databricks; they share how to use Flint with Apache Spark. Introduction The volume of data that data scientists face these days increases relentlessly, and we now find that a traditional, single-machine solution is no longer adequate to the demands […]

The post Introducing Flint: A time-series library for Apache Spark appeared first on Databricks.


          Podcast: Josh Hermsmeyer on NFL passing analytics      Cache   Translate Page      
On this episode of The Football Analytics Show, Josh Hermsmeyer, data scientist and writer for FiveThirtyEight, joins me to discuss his fascinating work on the NFL. Among other topics, we discuss: How a winemaker got into NFL dataHow to turn the distance the ball travels in the air, or air yards, into predictive numbersWhy teams […]
          CONSULTOR DATA SCIENCE BOGOTÁ - managementsolutions - Bogotá, Cundinamarca      Cache   Translate Page      
Recién titulados o estudiantes de último curso de Matemáticas, Físicas, Estadística, Econometría, Ingeniería u otros estudios con fuerte componente cuantitativo...
De managementsolutions - Sat, 21 Jul 2018 06:54:58 GMT - Ver todos: empleos en Bogotá, Cundinamarca
          Data Scientist - Yamaha - Cypress, CA      Cache   Translate Page      
Develop statistical models, machine learning-based tools or processes to measure and manage business performance....
From Yamaha - Wed, 22 Aug 2018 00:54:18 GMT - View all Cypress, CA jobs
          Data Scientist - FedEx Services - Brookfield, WI      Cache   Translate Page      
Women’s Business Enterprise National Council “America’s Top Corporations for Women’s Business Enterprises” - 2016....
From FedEx - Wed, 29 Aug 2018 00:08:19 GMT - View all Brookfield, WI jobs
          MapR packages services to help customers move up the AI learning curve      Cache   Translate Page      
MapR Technologies Inc. today announced six new data science service packages aimed at helping customers get started with machine learning and artificial intelligence. The customized offerings apply MapR data scientists to specific customer problems and projects to demonstrate the use of advanced analytics. The AI/ML Hack-a-thon has a MapR data science team working with an organization […]

The post MapR packages services to help customers move up the AI learning curve appeared first on SiliconANGLE.


          Principal Technologist - Machine Learning and Data Science - Blue Origin - Kent, WA      Cache   Translate Page      
While in this role, you will leverage your extensive experience in machine learning and data science to accelerate and innovate across business areas to drive...
From Blue Origin - Wed, 13 Jun 2018 05:31:45 GMT - View all Kent, WA jobs
          Data Scientist Job - SAIC - Reston, VA      Cache   Translate Page      
Work will be performed primarily with internal company contacts. Determines the appropriate analytics based on the data and the desired outcomes, using...
From SAIC - Sat, 08 Sep 2018 02:46:43 GMT - View all Reston, VA jobs
          Sr. Data Scientist - Data Visualization Specialist - Amazon.com - Seattle, WA      Cache   Translate Page      
You'll be required to figure out what's important to the business, to specific partners, and intuit core needs from people before they even realize they need it...
From Amazon.com - Thu, 19 Jul 2018 07:49:53 GMT - View all Seattle, WA jobs
          Global Data Science Platform Market Overview and Scope 2018 to 2025: Microsoft Corporation, IBM Corporation, Google, DataRobot      Cache   Translate Page      

Brooklyn, NY -- (SBWIRE) -- 09/12/2018 -- Qyresearchreports include new market research report Global Data Science Platform Market Size, Status and Forecast 2025 to its huge collection of research reports.

This report studies the global Data Science Platform market size, industry status and forecast, competition landscape and growth opportunity. This research report categorizes the global Data Science Platform market by companies, region, type and end-use industry.

In 2017, the global Data Science Platform market size was million US$ and it is expected to reach million US$ by the end of 2025, with a CAGR of during 2018-2025.

Market segment by Regions/Countries, this report covers
United States
Europe
China
Japan
Southeast Asia
India

Get Free PDF for more Professional and Technical insights @ https://www.qyresearchreports.com/sample/sample.php?rep_id=1865398&type=S

This report focuses on the global top players, covered
Microsoft Corporation
IBM Corporation
Google
Wolfram
DataRobot
Sense
RapidMiner
Domino Data Lab
Dataiku
Alteryx
Continuum Analytics
Datanami
YHat

Market segment by Type, the product can be split into
On-Premises
On-Demand

Market segment by Application, split into
Banking, Financial Services, and Insurance (BFSI)
Healthcare and Life Sciences
Information Technology and Telecom
Retail and Consumer Goods
Media and Entertainment
Manufacturing
Transportation and Logistics
Energy and Utilities
Government and Defense
Others

Browse full table of contents @ https://www.qyresearchreports.com/report/global-data-science-platform-market-size-status-and-forecast-2025.htm/toc

The study objectives of this report are:
To study and forecast the market size of Data Science Platform in global market.
To analyze the global key players, SWOT analysis, value and global market share for top players.
To define, describe and forecast the market by type, end use and region.
To analyze and compare the market status and forecast between China and major regions, namely, United States, Europe, China, Japan, Southeast Asia, India and Rest of World.
To analyze the global key regions market potential and advantage, opportunity and challenge, restraints and risks.
To identify significant trends and factors driving or inhibiting the market growth.
To analyze the opportunities in the market for stakeholders by identifying the high growth segments.
To strategically analyze each submarket with respect to individual growth trend and their contribution to the market
To analyze competitive developments such as expansions, agreements, new product launches, and acquisitions in the market
To strategically profile the key players and comprehensively analyze their growth strategies.

In this study, the years considered to estimate the market size of Data Science Platform are as follows:
History Year: 2013-2017
Base Year: 2017
Estimated Year: 2018
Forecast Year 2018 to 2025

For the data information by region, company, type and application, 2017 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

Key Stakeholders
Data Science Platform Manufacturers
Data Science Platform Distributors/Traders/Wholesalers
Data Science Platform Subcomponent Manufacturers
Industry Association
Downstream Vendors

Access the full Report @ https://www.qyresearchreports.com/report/global-data-science-platform-market-size-status-and-forecast-2025.htm

About QYResearchReports.com
QYResearchReports.com delivers the latest strategic market intelligence to build a successful business footprint in China. Our syndicated and customized research reports provide companies with vital background information of the market and in-depth analysis on the Chinese trade and investment framework, which directly affects their business operations. Reports from QYResearchReports.com feature valuable recommendations on how to navigate in the extremely unpredictable yet highly attractive Chinese market.

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For more information on this press release visit: http://www.sbwire.com/press-releases/global-data-science-platform-market-overview-and-scope-2018-to-2025-microsoft-corporation-ibm-corporation-google-datarobot-1047211.htm

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          FICO Makes Artificial Intelligence Explainable with Latest Release of its Analytics Workbench      Cache   Translate Page      
Leading analytics firm FICO announced the latest version of FICO® Analytics Workbench™, a cloud-based advanced analytics development environment that empowers business users and data scientists with sophisticated, yet easy-to-use, data exploration, visual data wrangling, decision strategy design and machine learning.
          Senior Data Scientist - Prudential - Newark, NJ      Cache   Translate Page      
Identify analytical solutions for business problems. And a passion for generating business impact. Develop and maintain consultative relationships with key...
From Prudential - Wed, 11 Jul 2018 21:51:46 GMT - View all Newark, NJ jobs
          Data Scientist - Prudential - Newark, NJ      Cache   Translate Page      
And a passion for generating business impact. Develop and maintain consultative relationships with key business stakeholders....
From Prudential - Wed, 11 Jul 2018 21:49:38 GMT - View all Newark, NJ jobs
          Data Scientist - TECHNICA CORPORATION - Dulles, VA      Cache   Translate Page      
Technica Corporation is seeking a Senior Data Scientist. To support our internal Innovation, Research and....
From Technica Corporation - Sat, 08 Sep 2018 10:29:14 GMT - View all Dulles, VA jobs
          Data Scientist - Toronto - Prodigy Game - Toronto, ON      Cache   Translate Page      
Prodigy Game will provide accommodations to job applicants with disabilities throughout the recruitment process....
From Prodigy Game - Fri, 27 Jul 2018 20:10:48 GMT - View all Toronto, ON jobs
          Data Scientist - Royal Sporting House - UAE      Cache   Translate Page      
Maintain a good technical knowledge of analytical and modelling software. No two days are the same at Al-Futtaim, no matter what role you have....
From Royal Sporting House - Sun, 19 Aug 2018 14:00:39 GMT - View all UAE jobs
          Scientist, Data Science (1 of 2) - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Consideration will be given to Raritan, NJ; Janssen Research &amp; Development LLC, a Johnson &amp; Johnson company, is recruiting for a Scientist, Data Science....
From Johnson & Johnson Family of Companies - Wed, 05 Sep 2018 16:05:17 GMT - View all Spring House, PA jobs
          Senior Scientist, Data Science (1 of 2) - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Consideration will be given to Raritan, NJ; Janssen Research &amp; Development LLC, a Johnson &amp; Johnson company, is recruiting for a Senior Scientist, Data Science....
From Johnson & Johnson Family of Companies - Wed, 05 Sep 2018 16:05:17 GMT - View all Spring House, PA jobs
          Senior Analyst, Data Science (1 of 2) - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Consideration will be given to Raritan, NJ; Janssen Research &amp; Development LLC, a Johnson &amp; Johnson company, is recruiting for a Senior Analyst, Data Science....
From Johnson & Johnson Family of Companies - Wed, 05 Sep 2018 04:05:45 GMT - View all Spring House, PA jobs
          Instructor (Data Science, Artificial Intelligence, Machine Learning) - Cortechma Inc. - Thornhill, ON      Cache   Translate Page      
Cortechma Academy team is looking for professors, instructors and engineers with both academically and professionally strong background specializing in one of...
From Indeed - Wed, 01 Aug 2018 16:56:17 GMT - View all Thornhill, ON jobs
          Top KDnuggets tweets, Sep 5-11: 10 Free Must-Read Books for Machine Learning and Data Science      Cache   Translate Page      
Also: Data Science Cheat Sheet; Machine Learning Cheat Sheets; Journey to Machine Learning - 100 Days of ML Code.
          Top August Stories: Data Visualization Cheat Sheet; Basic Statistics in Python      Cache   Translate Page      
Also: Eight iconic examples of data visualisation; Data Scientist guide for getting started with Docker.
          ​Hollywood goes open source      Cache   Translate Page      
https://www.zdnet.com/article/hollywood-goes-open-source

Out of 200 of the most popular movies of all time, the top 137 were either visual-effects driven or animated. What did many of these blockbusters have in common? They were made with open-source software.

That was the message David Morin, chairman of the Joint Technology Committee on Virtual Production, brought to The Linux Foundation's Open Source Summit in Vancouver, Canada. To help movie makers bring rhyme and reason to open-source film-making, The Linux Foundation had joined forces with The Academy of Motion Picture Arts and Sciences to form the Academy Software Foundation.

The academy is meant to be a neutral forum for open-source developers both in the motion picture and broader media industries to share resources and collaborate on technologies for image creation, visual effects, animation, and sound. The founding members include Blue Sky Studios, Cisco, DreamWorks Animation, Epic Games, Google Cloud, Intel, Walt Disney Studios, and Weta Digital. It's a true marriage of technology and media-driven businesses.
You know those names. You probably don't know the name of the open-source, special-effects programs, such as Alembic, OpenColorIO, or Ptex, but Morin said, "they're very instrumental in the making of movies".

And they're more important than you think. "The last Fast and the Furious movie, for instance, while it looks like a live-action movie, when you know how it was made, it's really by-and-large a computer generated movie," Morin said. "When Paul Walker passed away in the middle of production, he had to be recreated for the duration of the movie."

The Academy of Motion Picture Arts and Sciences, which you know best from the Oscars, started looking into organizing the use of open-source in the movies in 2016. The group did so because while open-source software was being used more and more, it came with problems. These included:
  • Versionitis: As more libraries were being used it became harder to coordinate software components. A production pipeline, which had been perfected for a 2016 movie, is likely to have out-of-date components for a 2018 film.
  • Organization: While volunteers tried to track these changes, they didn't have the funding or resources needed to go beyond recording changes.
  • Funding: Many open-source programs had lost their maintainers due to getting jobs elsewhere or for lack of funding.
  • Licensing: As all open-source developers know, sooner or later licensing becomes an issue. That's especially true in the motion-picture industry, which is hyper aware of copyright and other intellectual property (IP) issues.
So, the overall mission is to increase the quality and quantity of open-source contributions by developing a governance model, legal framework, and community infrastructure that makes it easier to both develop and use open-source software.
In more detail, the goals are:
  • Provide a neutral forum to coordinate cross-project efforts, establish best practices, and share resources across the motion picture and broader media industries.
  • Develop an open continuous integration (CI) and build infrastructure to enable reference builds from the community and alleviate issues caused by siloed development.
  • Provide individuals and organizations with a clear path for participation and code contribution.
  • Streamline development for build and runtime environments through the sharing of open-source build configurations, scripts, and recipes.
  • Provide better, more consistent licensing through a shared licensing template.
Developers interested in learning more or contributing can join Academy Software Foundation mailing list.
Morin added, "In the last 25 years, software engineers have played an increasing role in the most successful movies of our time. The Academy Software Foundation is set to provide funding, structure, and infrastructure for the open-source community, so that engineers can continue to collaborate and accelerate software development for movie making and other media for the next 25 years."
Rob Bredow, SVP, executive creative director, and head of Industrial Light & Magic, said, "Developers and engineers across the industry are constantly working to find new ways to bring images to life, and open source enables them to start with a solid foundation while focusing on solving unique, creative challenges rather than reinventing the wheel."
If you'd like to get into the movie business, now's your chance. "We're welcoming all the help we can get to set up the foundation," Morin concluded. "Writing code today is perhaps the most powerful activity that you can do to make movies. If you're interested, don't hesitate to join us."
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          How data analytics-rich business networks help close the digital transformation gap      Cache   Translate Page      
The next BriefingsDirect thought leadership discussion explores how intelligence gleaned from business applications, data, and networks provides the best new hope for closing the digital transformation gap at many companies.

A recent global survey of procurement officers shows a major gap between where companies are and where they want to be when it comes to digital transformation. While 82 percent surveyed see digital transformation as having a major impact on processes -- only five percent so far see significant automation across their processes.

How can business networks and the cloud-based applications underlying them better help companies reach a more strategic level of business intelligence and automation?

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy

To find out, BriefingsDirect recently visited SAP in Palo Alto, Calif. to sit down with Darren Koch, Chief Product Officer at SAP Ariba. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner:What's holding companies back when it comes to becoming more strategic in their processes? They don’t seem to be able to leverage intelligence and automation to allow people to rise to a higher breed of productivity.

Koch
Koch: I think a lot of it is inertia. The ingrained systems and processes that exist at companies impact a lot of people. The ability for those companies to run their core operations relies on people and technology working together. The change management required by our customers as they deploy solutions -- particularly in the move from on-premises to the cloud -- is a major inhibitor.

But it's not just the capabilities and the change in the new technology. It's really re-looking at -- and reimagining -- the processes, the things that existed in the highly customized on-premises world, and the way those things change in a digital-centric cloud world. They are fundamentally different. 

Gardner: It's always hard to change behavior. It seems like you have to give people a huge incentive to move past that inertia. Maybe that's what we are all thinking about when we bring new data analytics capabilities to bear. Is that what you looking at, incentivization -- or how do we get that gap closed?

Reimagining change in the cloud

Koch: You are seeing more thought leadership on the executive side. You are seeing companies more willing to look holistically at their processes and saying, “Is this something that truly differentiates my company and adds sustainable competitive advantage?” And the answer on some processes is, “No."


And so, we see more moving away from the complex, on-premises deployments that were built in a world where a truckload of consultants would show up and configure your software to do exactly what you wanted. Instead, we’re moving to a data-centric best-practices type of world that gives scale, where everybody operates in the same general business fabric. You see the emergence of things like business networks.

Gardner: And why the procurement and supply chain management folks? Why are they in an advantageous position to leverage these holistic benefits, and then evangelize them?

Koch: There'sbeen a ton of talk and innovation on the selling side, on the customer resource management (CRM) side, such as our announcement of C/4HANAat Sapphire 2018 and the success in the cloud generally in the CRM space. What most people stop at is, for every seller there's a buyer. We represent the buy-side, the supply chain, the purchasing departments. And now from that buy-side we have the opportunity to follow the same thought processes on the sell-side.

The beauty at SAP Ariba is that we have the world's biggest business network. We have over $2 trillion of buy-side spend and our ability to take that spend and find real insights and real actionable change to drive value at the intersection of buyers and sellers. This is where we’re headed.

Gardner: It seems like we are moving rapidly beyond the buy and sell being just transactional and moving more to deeper partnerships, visibility, of understanding the processes on both sides of the equation. That can then bring about a whole greater than the sum of the parts.

Understanding partners 

Koch: Exactly. I spent 10 years working in the consumer travel space, and my team in particular was working on how consumers choose hotels. It's a very complex purchasing decision.

There are location aspects, there are quality aspects, there are amenities, room size, obviously price, and there are a lot of non-price actors that go into the purchase decision, too. When you look at what a procurement audience is doing, what a company is doing, there are a lot of such non-price factors. It’s exactly the same problem.

The investments that we are making inside of SAP Ariba get at allowing you to see things like supplier risk. You are seeing things like the Ariba Network handling direct materials. You are seeing time, quality, and risk factors -- and these other non-price dimensions -- coming in, in the same way that consumers do when choosing a hotel. Nobody chooses the cheapest one, or very few people do. Usually it’s a proper balance of all of these factors and how they best meet the total needs. We are seeing the same thing on the business procurement side.
When you look at what a procurement audience is doing, what a company is doing, there are now a lot of non-price factors.

Gardner: As consumers we have information at our fingertips -- so we can be savvy and smart – probably better than at any other time in history. But that doesn’t always translate to a larger business-to-business (B2B) decisions.

What sort of insights do you think businesses will want when it comes that broader visibility?

Koch: It starts with the basics. It starts with, “How do I know my suppliers? How do I add scale? Is this supplier General Data Protection Regulation (GDPR)-compliant? Do they have slavery or forced labor in their supply chain? Where are they sourcing their materials?” All of these aspects around supplier risk are the basics; knowing your supplier well is the basic element.

Then when you go beyond that, it's about things like, “Well how do I weigh geographic risk? How do I weigh supply chain risk?” And all the things that the practitioners of those disciplines have been screaming about for the rest of their companies to pay attention to.

That’s the new value they are providing. It's that progression and looking at the huge opportunity to see the way companies collaborate and share data strategically to drive efficiency into processes. That can drive efficiency ultimately into the whole value chain that leads to a better customer experience at the end.

Gardner: Customer experience is so important across the board. It must be a big challenge for you on the product side to be able to contextually bring the right information and options to the end-user at the right time. Otherwise they are overwhelmed, or they don't get the benefit of what the technology and the business networks can do.

What are you doing at SAP Ariba to help bring that right decision-making -- almost anticipating where the user needs to go -- into the actual applications and services?

Intelligent enterprise

Koch: That begins with our investments in re-platforming to SAP HANA. That feeds into the broader story about the intelligent enterprise. Purchasing is one facet, supply-chain management is a facet, sales is a facet, and production -- all of these components are elements of a broader story of how you synthesize data into a means where you have a digital twin of the whole enterprise.

Then you can start doing things like leveraging the in-memory capabilities of HANA around scenario planning, and around, “What are the implications of making this decision?”

What happens when a hurricane hits Puerto Rico and your supply chain is dramatically disrupted? Does that extend to my suppliers’ suppliers?  Who are my people on the ground there, and how are they disrupted? How should my business respond in an intelligent way to these world events that happen all the time?

Gardner: We have talked about the intelligent enterprise. Let's hypothetically say that when one or two -- or a dozen -- enterprises become intelligent that they gain certain advantages, which compels the rest of their marketplace to follow suit.

When we get to the point where we have a critical mass of intelligent enterprises, how does that elevate to an intelligent economy? What can we do when everyone is behaving with this insight, of having tools like SAP Ariba at their disposal?

Koch: You hit on a really valuable and important point. Way back, I was an economics major and there was a core thing that I took away 20 years ago from my intro to macroeconomics class. The core of it was that everything is either value or waste. Every bit of effort, everything that's produced around the world, all goods or services are either valuable or a waste. There is nothing in between.

The question then as we look at value chains, when we look at these webs of value, is how much of that is transaction cost? How much of that is information asymmetry? How much of that is basic barriers that get in the way of ultimately providing value to the end consumer? Where is all of that waste?

When you look at complex value chains, at all of the inventory sitting in warehouses, the things that go unsold, the mismatches between supply and demand across a value chain -- whether you are talking about direct materials or about pens and paper sitting in a supply closet -- it really doesn't matter.
When you look at complex value chains ... how much of that goes into actually delivering on what your customers and employees value -- and how much of it is waste?

It’s all about how much of that goes to actually delivering on what your customers, your employees, and your stakeholders’ value -- and how much of it is waste? As we link these data sets together -- the real production planning, understanding end-user demand, and all the way back through the supply chain – we can develop new transparency that brings a ton of value. And by ultimately everyone in the value chain understanding what the consumers’ actually value, then they can innovate in the right ways.

So, I see this all dramatically changing as you link these intelligent companies together. As companies move in the same way -- into a sharing mindset – then the sharing economy uses resources in a far more efficient way, in the exact same way as we use our data resources in a more efficient way.

Gardner: This also dovetails well with being purposeful as a business. If many organizations are encouraging higher productivity, which reduces inefficiencies and helps raise wages, it can lead to better standards of life. So, the stakes here are pretty high.

We’re not just talking about adding some dollars to the bottom and top lines. We’re also talking about a better economy that raises all boats.

Purposeful interconnections 

Koch: Yes, absolutely. You see companies like Johnson and Johnson, who at their core, from their founding principles, have the importance of their community as one of the core founding principles. You see it in companies like Ford and their long heritage. Those ideals are really coming back from the decade of the 1980s where greed was good and now back to a more holistic understanding of the interconnectedness of all of this.

And it’s good as humans. It’s also good from the business perspective because of the need to attract and retain the talent required to run a modern enterprise. And building the brands that our consumers are demanding, and holding companies accountable, they all go hand-in-hand.

And so, the purpose aspect really addresses the broader stakeholder aspects of creating a sustainable planet, a sustainable business, sustainable employment, and things like that.

Gardner: When we think about attaining this level of efficiency through insights and predictive analytics -- taking advantage of business networks and applications and services -- we are also on the cusp of getting even better tools.

We’re seeing a lot more information about machine learning (ML). We’re starting to tease out the benefits of artificial intelligence (AI). When these technologies are maturing and available, you need to be in a position to take advantage of them.

So, moving toward the intelligent enterprise and digital transformation are not just good or nice to have, they are essential because of what's going to come next in just a few years.

Efficiency in the digital future 

Koch: Yes, you see this very tactically in the chief procurement officers (CPOs) that I've talked with as I've entered this role. I have yet to run across any business leader who says, “I have so many resources, I don't know what to do.” That’s not usually what I hear. Usually, it's the opposite. It’s, “I'm being asked to do more with less.”

When you look at the core of AI, and the core of ML, it’s how do you increase efficiency? And that’s whether it's all the way on the full process automation side, or it’s along the spectrum of bringing the right intelligence and insights to streamline processes to make better decisions.

All of that is an effort to up-level the work that people do, so that raises wages, it raises productivity, all of those things. We have an example inside of our team. I was meeting with the head of our customer value organization, Chris Haydon, over dinner last night.  Chris was talking about how we were applying ML to enhance our capability to onboard new customers.

And he said the work that we've done has allowed him to redeploy 80 people in his team on to higher productivity use cases. All of those people became more valuable in the company because they were working on things that were at the next level of creating new solutions and better customer experiences, instead of turning the crank in the proverbial factory of deploying software.

Gardner: I happen to personally believe that a lot of the talk about robots taking over people’s jobs is hooey. And that, in fact, what's more likely is this elevation of people to do what they can do best and uniquely. Then let the machines do what they do best and uniquely.

How is that translating both into SAP Ariba products and services, and also into the synergy between SAP and SAP Ariba?
We're just getting through a major re-platforming to S/4 HANA and that's really exciting because of HANA's maturity and scale. We're using ML algorithms and applying them.

Koch: We are at a really exciting time inside of our products and services. We're just getting through a major re-platforming to S/4 HANA, and that’s really exciting because of HANA’s maturity and scale. It’s moving beyond basic infrastructure in the way that [SAP Co-Founder] Hasso Plattner had envisioned it.

We’re really getting to the point of not replicating data. We are using the ML algorithms and applying them, building them once and applying them at large. And so, the company’s investments in HANA and in Leonardo are helping to create a toolkit of capabilities that applications like SAP Ariba can leverage. Like with any good infrastructure investment, when you have the right foundation you see scale and innovation happen quickly.

You'll see a lot more of how we leverage the data that we have both inside the company as well as across the network to drive intelligence into our process. You will just see that come through more as we move from the infrastructure foundation setting stage to building the capabilities on top of that.

Gardner: Getting back to that concept of closing the transformation gap for companies, what is it they should be thinking about when these services and technologies become available? How can they help close their own technology gap by becoming acquainted in advances and taking some initiative to best use these new tools?

Digital transformation leadership 

Koch: The companies that are forward-leading on digital transformation are the ones that made the cloud move early. The next big move for them is to tap into business networks. How can they start sharing across their value chains and drive higher efficiency? I think you'll see from that the shift from tactical procurement to strategic procurement.

The relationships need to move from transactional to a true partnership, of how do we create value together? That change involves rethinking the ways you look at data and of how you share data across value chains.

Gardner: Let’s also think about spend management conceptually. Congratulations, by the way, on your recent Gartner Magic Quadrant positioning on pay-to-procure processes. How does spend management also become more strategic?

Koch: The building blocks for spend management always come down to what is our tactical spend and where should we focus our efforts for strategic spend? Whether that is in the services area, travel, direct materials, or indirect, what customers are asking SAP for is, how do all of these pieces fit together?

What's the difference between a request for proposal (RFP) for a hotel in New York City versus an RFP for chemicals in Southeast Asia? They're both a series of business processes of selecting the right vendor that balances all of the critical dimensions: Price and everything else that makes for a good decision and that has longevity.

We see a lot of shared elements in the way you interact with your suppliers. We see a lot of shared elements in the way that you deploy applications inside of your company. We’re exploring how well the different facets of the applications can work together, how seamless the user experience is, and how well all of these tie together for all the stakeholders.

Ultimately, each element of the team, each element of the company, has a role to play. That includes the finance organization’s desire to ensure that value is being created in a way that the company can afford. It means that the shareholders, employees, management, and end-users are all on the same page.

This is the core of spend management – and the intelligent enterprise as a whole. It means being able to see everything, by bringing it all together, so the company can manage its full operations and how they create value.

Gardner: The vision is very compelling. I can certainly see where this is not going to be just a small change -- but a step-change -- in terms of how companies can benefit in productivity.

As you were alluding to earlier, architecture is destiny when it comes to making this possible. By re-architecting around, as for S/4 HANA, by taking advantage of business networks, you are well on the way to delivering this. Let’s talk about the platform changes that grease the skids toward the larger holistic benefits.

Shifting to the cloud 

Koch: It's firmly our belief that the world is moving to mega-platforms. SAP has a long history of bringing the ecosystem along, whether the ecosystem is delivering process innovation or is building capabilities on top of other capabilities embedded deeply into the products.

What we're now seeing is the shift from the on-premises world to a cloud world where it's API-first, business events driven, and where you see a decoupling of the various components. Underneath the covers it doesn't matter what technology stack things are built on. It doesn't matter how quickly they evolve. It's the assumption that we have this API contract between two different pieces of technology: An SAP Ariba piece of technology, an SAP S/4 Cloud piece of technology, or a partner ecosystem piece of technology.

For example, a company like Solenis was recently up on stage with us at Ariba Live in Amsterdam. That's one of the fastest-growing companies. They have raised a B round at $1 billion valuation. Having companies that are driving innovation like that in partnership with an SAP platform brings not just near-term value for us and our customers, it brings future-proofing. It brings extensibility when there is a specific requirement that comes in for a specific industry or geography. It provides a way a customer can differentiate. You can just plug-in.
We're now seeing the shift from on-premises to cloud where you see a decoupling of the components. It doesn't matter what the technology stack is. ... It's now about API-first business events.

[SAP business unit] Concur has been down this path for a long time. The president of SAP Ariba, Barry Padgett, actually started the initiative of opening up the Concur platform. So deep at our core -- in our roots -- we believe that networks, ecosystems, and openness will ensure that our customers get the most value out of their solutions.

Gardner:Because SAP is an early adopter of multicloud, SAP can be everywhere at the most efficient level given what the hyperscale cloud providers are providing with global reach and efficiency. This approach also allows you to service small- to medium-sized businesses (SMBs), for example, essentially anywhere in the world.

Tell me why this long-term vision of a hyperscale-, multicloud-supported future benefits SAP, SAP Ariba, and its customers.

A hyperscale, multicloud landscape

Koch: When you look across the landscape of the hyperscalers and you look at the pace of innovation and the level of scale that that they are able to deliver, our lead time is slashed. We can also scale up and down as required. The cloud benefits apply to speed compared to having boxes installed in data centers, as well as ease in workload variability -- whether it's test variability or our ability to run ML-training models.

The idea that we still suffer multi-month lead times to get our physical boxes installed in our data centers is something that we just can't afford. Our customers demand more.

Thankfully there are multiple solutions around the world that solve these problems while at the same time giving us things like world-class security, geographic footprints, and localized expertise. When a server fails halfway around the world and the expert is somewhere else, the hyperscalers provide a solution to that problem.

They have somebody who walks through every data center and makes sure that the routers are upgraded, and the switches and load balancers are working the way they should. They determine whether data correctly rests inside of a Chinese firewall or inside of Europe [due to compliance requirements]. They are responsible for how those systems interact.

We still need to do our investment on the applications tier and in working with our customers to handle all of the needed changes in the landscape around data and security.

But the hyperscalers give us a base-level of infrastructure so we don't need to think about things like, “Is our air conditioner capacity inside of the data center sufficient to run the latest technology for the computing power?” We don't worry about that. We worry about delivering value on top of that base-level of infrastructure and so that takes our applications to the next level.

In the same way we were talking earlier about ML and AI freeing up our resources to work on higher-value things, [the multicloud approach] allows us to stop thinking about these base-level things that are still critical for the delivery of our service. It allows us to focus on the innovation aspects of what we need to do.

Gardner: It really is about driving value higher and higher and then making use of that in a way that's a most impactful to the consumers -- and ultimately the whole economy.

Koch: You got it.


          Principal Market Validation Specialist - PTC - Needham, MA      Cache   Translate Page      
Advance knowledge and experience with Machine Learning / Data Science / Analytics. Customer Satisfaction focus, both internal and external, with strong...
From PTC - Wed, 16 May 2018 14:29:21 GMT - View all Needham, MA jobs
          Senior Data Scientist Consultant - GI Research - Milano, Lombardia      Cache   Translate Page      
The successful candidate will be a valid partner of the Business Developer Data Services contributing and developing the current retail and supplier client...
Da GI Research - Thu, 23 Aug 2018 17:20:19 GMT - Visualizza tutte le offerte di lavoro a Milano, Lombardia
          Data entry/Data Capture - Upwork      Cache   Translate Page      
We require a motivated person to find the main contact number of companies. A spreadsheet of company names will be provided. We just need to check that they have a google maps listing with a telephone number. The number will then need to be cut and pasted into the correct field in the spreadsheet provided. We need as many gathering a possible everyday.

Posted On: September 13, 2018 07:57 UTC
ID: 214191477
Category: Data Science & Analytics > Machine Learning
Skills: Data Entry, Virtual Assistant
Country: United Kingdom
click to apply
          Social Media / Marketing Analytics Consultant - Upwork      Cache   Translate Page      
Looking for an individual with strong experience in digital marketing analytics and data modeling, to assist in creating models for a new social media analytics platform.

Looking to get started immediately with the right candidate.

Posted On: September 13, 2018 07:53 UTC
ID: 214191154
Category: Data Science & Analytics > Quantitative Analysis
Skills: Data Analytics, Digital Marketing, Social Media Marketing
Country: United States
click to apply
          DevOps Engineer Python Agile Docker      Cache   Translate Page      
DevOps Engineer (Python OO Linux Cloud Kubernetes Docker Jenkins). Utilise your DevOps Engineer skills within a successful data science consultancy that is working with some of the best software vendors in the industry on a range of interesting projects such as Data Lake solutions, Blockchain projects and IoT development. The company cultivate a continuous learning environment enabling you to stay ahead of the game with the latest industry trends and upon starting will enrol you on a course that covers Big Data, DevOps and Data Science allowing you to perform to the best of your ability. As a DevOps Engineer you will be acting as a consultant, travelling to a variety of London based clients and participating in leading edge projects. You will be required to provide hands-on technical expertise for clients utilising the best of Open Source software on premise and in the Cloud. This is the first DevOps hire within the London office meaning you will be able to make the role your own and have the opportunity to take on a leadership position, building a successful team around you. Based in London, you will be joining a friendly and supportive company that will encourage you to continually develop new skills allowing you to reach your full potential. Requirements: *Experience with DevOps culture and Agile project delivery *Software development background using any OO programming language (Java, C++, C#) *Strong Python skills *Experience with containerisation and deployment tools (Docker, Kubernetes, Jenkins) *Good Linux knowledge *Cloud experience *Able to travel to client sites across London *Excellent communication skills As a DevOps Engineer (Python) you can expect to earn a competitive salary (up to £85k) plus benefits. Apply today or call to have a confidential discussion about this DevOps Engineer (Python) role.
          Lejce dla sztucznej inteligencji, czyli uczenie maszynowe w praktyce      Cache   Translate Page      

Nauka, dane i biznes Nauka o danych (ang. data science), jako coś więcej niż „zwykła” statystyczna analiza danych, to wbrew pozorom wcale nie nowa gałąź informatyki. Pierwsze rozwiązania, zwłaszcza algorytmy oraz metody zostały opracowane w latach 50. i 60. ubiegłego wieku (https://en.wikipedia.org/wiki/Timeline_of_machine_learning). Niemniej, z powodu względnie małego poziomu informatyzacji procesów produkcyjnych (również tzw. biznesowych), powodującego […]

Artykuł Lejce dla sztucznej inteligencji, czyli uczenie maszynowe w praktyce pochodzi z serwisu Linux Polska - Open Source Company.


          Data Scientist - Game Hive - Toronto, ON      Cache   Translate Page      
Our most popular games include the Tap Titans, Beat the Boss and Antrim Escape series (and many, more!). Game Hive is building a new generation of casual mobile...
From Indeed - Wed, 15 Aug 2018 13:34:49 GMT - View all Toronto, ON jobs
          SAS Viya: Neue Version macht KI und Machine Learning transparent      Cache   Translate Page      
Mehr Nachvollziehbarkeit für KI-gestützte Entscheidungen hilft den Anwendern und dem Datenschutz SAS, einer der weltgrößten Softwarehersteller, erhöht mit der aktuellen Version von SAS Viya die Transparenz von analytischen Modellen aus neuen Machine-Learning-Technologien. So sollen sowohl Data Scientists als auch fachfremde Nutzer analytische Modelle leicht verstehen und anpassen können. Zudem baut die neue Version die Integration […]
          Data Scientist / Operations Research Engineer - 67712 - Advanced Micro Devices, Inc. - Austin, TX      Cache   Translate Page      
Work closely with the business units to identify Machine Learning applications, define the strategic and tactical needs and drive the appropriate business...
From Advanced Micro Devices, Inc. - Thu, 12 Jul 2018 07:32:54 GMT - View all Austin, TX jobs
          Faculty Member, Computer Science (Databases and Data Science) - University of Saskatchewan - Saskatoon, SK      Cache   Translate Page      
The University of Saskatchewan is located on Treaty 6 territory and homeland of the Métis and is located in Saskatoon, Saskatchewan, a city with a diverse and...
From University of Saskatchewan - Fri, 27 Jul 2018 00:18:50 GMT - View all Saskatoon, SK jobs
          Sales Engineer - Hitachi Vantara - New York, NY      Cache   Translate Page      
Account Managers, internal specialists and customers. Understanding of Data Science and Machine Learning....
From Hitachi Vantara - Sat, 04 Aug 2018 04:47:47 GMT - View all New York, NY jobs
          Data Scientist - FedEx Services - Brookfield, WI      Cache   Translate Page      
Women’s Business Enterprise National Council “America’s Top Corporations for Women’s Business Enterprises” - 2016....
From FedEx - Wed, 29 Aug 2018 00:08:19 GMT - View all Brookfield, WI jobs
          Principal Technologist - Machine Learning and Data Science - Blue Origin - Kent, WA      Cache   Translate Page      
While in this role, you will leverage your extensive experience in machine learning and data science to accelerate and innovate across business areas to drive...
From Blue Origin - Wed, 13 Jun 2018 05:31:45 GMT - View all Kent, WA jobs
          Data Scientist Job - SAIC - Reston, VA      Cache   Translate Page      
Work will be performed primarily with internal company contacts. Determines the appropriate analytics based on the data and the desired outcomes, using...
From SAIC - Sat, 08 Sep 2018 02:46:43 GMT - View all Reston, VA jobs
          2019 Internship - Bellevue, WA- Data Science - Expedia - Bellevue, WA      Cache   Translate Page      
June 17 – September 6. As a Data Scientist Intern within Expedia Group, you will work with a dynamic teams of product managers and engineers across multiple...
From Expedia - Fri, 31 Aug 2018 21:36:06 GMT - View all Bellevue, WA jobs
          Principal / Senior Software Engineer - RichRelevance - Seattle, WA      Cache   Translate Page      
(Crunch, Cassandra, HBase, Hive, Presto, no-SQL databases). Principal / Senior Software Engineer - Data Science Engineering....
From RichRelevance - Sun, 27 May 2018 09:38:05 GMT - View all Seattle, WA jobs
          Data Science Intern - Zillow Group - Seattle, WA      Cache   Translate Page      
Interest in working with multi-terabyte-sized data sets and are comfortable accessing those data (even if they're in JSON format) with Hive and Presto....
From Zillow Group - Wed, 12 Sep 2018 01:06:00 GMT - View all Seattle, WA jobs
          Senior Software Engineer, Cloud Engineering - ExtraHop Networks, Inc. - Seattle, WA      Cache   Translate Page      
Experience with data science information processing pipeline (Spark / Presto / SQL / Hadoop / HBASE). Big Data, the cloud, elastic computing, SaaS, AWS, BYOD,...
From ExtraHop Networks, Inc. - Tue, 11 Sep 2018 18:44:58 GMT - View all Seattle, WA jobs
          Data Scientist, Zillow Offers - Zillow Group - Seattle, WA      Cache   Translate Page      
Dive into Zillow's internal and third party data (think Hive, Presto, SQL Server, Python, R, Tableau) to make strategic recommendations (e.g., improve...
From Zillow Group - Thu, 06 Sep 2018 01:06:28 GMT - View all Seattle, WA jobs
          Senior Data Scientist, Consumer Analytics - Zillow Group - Seattle, WA      Cache   Translate Page      
Extensive experience directly querying multi-terabyte-sized data sets (with Hive and Presto) including clickstream data (like Google Analytics), third party...
From Zillow Group - Wed, 05 Sep 2018 01:05:33 GMT - View all Seattle, WA jobs
          Faculty Member, Computer Science (Databases and Data Science) - University of Saskatchewan - Saskatoon, SK      Cache   Translate Page      
The University of Saskatchewan is located on Treaty 6 territory and homeland of the Métis and is located in Saskatoon, Saskatchewan, a city with a diverse and...
From University of Saskatchewan - Fri, 27 Jul 2018 00:18:50 GMT - View all Saskatoon, SK jobs
          Data Scientist - NetMotion Software - Victoria, BC      Cache   Translate Page      
There is a strong, collaborative relationship between managers, architects, developers, program managers, SDETs and QA engineers as partners in the delivery of...
From NetMotion Software - Thu, 13 Sep 2018 08:01:32 GMT - View all Victoria, BC jobs
          Mahasiswa Binus Raih Juara Pertama Kompetisi Data Science Se-ASEAN      Cache   Translate Page      

Liputan6.com, Jakarta - SAP dan ASEAN Foundation kembali mengadakan kompetisi data science tahun keduanya. Adapun kompetisi bernama ASEAN Data Science Explorer (DSE) tersebut ditutup dengan babak final tingkat nasional, yang dihelat di Universitas Indonesia.

Kompetisi ini diikuti dari peserta mahasiswa dari seluruh Indonesia. Para pemenang dianugerahi tiga penghargaan teratas untuk wawasan dan ide-ide inovatif mereka terkait menangani isu-isu sosial dan ekonomi di kawasan ASEAN.

Bertajuk "Anak Muda Masa Kini untuk Masa Depan Dunia," kompetisi ini memungkinkan anak muda dari negara-negara anggota ASEAN memegang peran kunci dalam mengatasi masalah sosial saat ini dan membantu menciptakan perubahan positif untuk masa depan yang lebih baik.

Tim OWL dari Universitas Bina Nusantara (Binus), berhasil menduduki posisi juara di Babak Final Tingkat Nasional.

Sementara itu, posisi runner-up pertama diraih Tim Gray Matters, dan runner-up kedua diperoleh Tim Ganesha. Kedua tim runner-up berasal dari Institut Teknologi Bandung.

“Berpartisipasi dalam ADSE merupakan peluang besar bagi kami untuk melaksanakan peran kami dalam mengubah dunia. Teknologi SAP telah membantu kami mempelajari bagaimana kuatnya data dapat menciptakan perubahan dan membantu kami berkontribusi terhadap pembangunan berkelanjutan di ASEAN,” kata Angelia Dwi Handoko and Tasia Rosalina Tedjo Purnomo, salah satu tim finalis dari Universitas Surya.

“Untuk mengatasi permasalahan rumit seperti dalam Tujuan Pembangunan Berkelanjutan PBB, anak muda kita harus terbiasa dengan teknologi terbaru untuk menemukan solusi baru untuk masalah saat ini dan masa depan," ujar Andreas Diantoro, Managing Director SAP Indonesia, yang juga menjadi bagian dari panel juri untuk kompetisi, di Jakarta, Kamis (13/9/2018).

"ASEAN Data Science Explorers (ADSE) adalah langkah pertama bagi anak muda Indonesia untuk bekerja memecahkan masalah dunia dengan menggunakan data dan analisis yang dapat diukur secara nyata,” tambahnya.

 

SAP Analytics Cloud

Babak Final Tingkat Nasional kompetisi ASEAN Data Science Explorer (DSE) SAP dan ASEAN Foundation. Liputan6.com/Jeko I.R.#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

Para mahasiswa yang mendaftar untuk kompetisi, memperoleh akses ke data ASEAN serta platform SAP Analytics Cloud, yang memungkinkan mereka menganalisis data dan mendapatkan wawasan.

SAP Analytics Cloud sendiri merupakan generasi baru dari Software-as-a-Service (SaaS) yang mendefinisikan ulang analytics dalam cloud dengan menyediakan intelijen bisnis (BI), kemampuan prediksi, dan kemampuan perencanaan dalam satu perangkat tunggal.

Dengan menggunakan platform cloud tersebut, para peserta dapat menganalisis dan memvisualisasikan data untuk menghasilkan rekomendasi inovatif mereka.

 

Kriteria Penilaian

Data Scientist | via: analyticsbodhi.com#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

Adapun penilaian ini didasarkan pada tiga kriteria: relevansi dan kredibilitas data, kualitas wawasan analitis, dan kelayakan solusi yang direkomendasikan untuk masalah yang ditangani oleh masing-masing proyek.

Dari 11 finalis, peraih tempat pertama akan melanjutkan kompetisi ke Babak Final Tingkat Regional yang akan berlangsung pada 25 Oktober 2018 di Singapura, di mana mereka akan menghadapi pemenang teratas dari putaran Babak Final Tingkat Nasional lainnya.

Sekadar informasi, ini menjadi tahun kedua ASEAN DSE diselenggarakan di seluruh 10 negara anggota ASEAN sebagai bagian dari kemitraan strategis antara ASEAN Foundation dan SAP.

Kompetisi dihelat pertama kali pada 2017, ASEAN Data Science Explorers (ADSE) adalah kompetisi data analytics yang bertujuan untuk meningkatkan kesadaran dan apresiasi terhadap komunitas ASEAN di antara anak muda melalui intervensi literasi digital.

Dengan menggunakan SAP Analytics Cloud, para kontestan didorong untuk mengembangkan wawasan berbasis data yang menyoroti berbagai isu di ASEAN yang dibahas dalam enam Tujuan Pembangunan Berkelanjutan Perserikatan Bangsa-Bangsa (PBB), yaitu kesehatan dan kesejahteraan yang baik, pendidikan berkualitas, kesetaraan gender, pekerjaan yang layak dan pertumbuhan ekonomi, industri, inovasi & infrastruktur, dan kota dan masyarakat yang berkelanjutan.

(Jek/Isk)

Saksikan Video Pilihan Berikut Ini:


          Research Associate (Data Scientist) - Singapore University of Technology and Design - Changi      Cache   Translate Page      
Vue.js and d3.js do the heavy lifting on the frontend, while our backend software is mostly written in node.js and R (tidyverse)....
From Singapore University of Technology and Design - Mon, 23 Jul 2018 05:21:57 GMT - View all Changi jobs
          Data Scientist - Game Hive - Toronto, ON      Cache   Translate Page      
Our most popular games include the Tap Titans, Beat the Boss and Antrim Escape series (and many, more!). Game Hive is building a new generation of casual mobile...
From Indeed - Wed, 15 Aug 2018 13:34:49 GMT - View all Toronto, ON jobs
          Orchestrating Kubernetes with Chris Gaun      Cache   Translate Page      

A company runs a variety of distributed systems applications such as Hadoop for batch processing jobs, Spark for data science, and Kubernetes for container management. These distributed systems tools can run on-prem, in a cloud provider, or in a hybrid system that uses on-prem and cloud infrastructure. Some enterprises use VMs, some use bare metal,

The post Orchestrating Kubernetes with Chris Gaun appeared first on Software Engineering Daily.


          Faculty Member, Computer Science (Databases and Data Science) - University of Saskatchewan - Saskatoon, SK      Cache   Translate Page      
The University of Saskatchewan is located on Treaty 6 territory and homeland of the Métis and is located in Saskatoon, Saskatchewan, a city with a diverse and...
From University of Saskatchewan - Fri, 27 Jul 2018 00:18:50 GMT - View all Saskatoon, SK jobs
          Senior Data Scientist - Permanent - London      Cache   Translate Page      
Data Idols - Central London - industry and has given millions of people worldwide greater access to an everyday service. They are currently seeking a Senior Data Scientist...
          Director Data Science (Insights & Analytics) - US Foods - Chicago, IL      Cache   Translate Page      
Excellent business acumen. Business Strategy and Analytics. Experience developing models, monitoring them in production, and measuring their business impact....
From US Foods - Thu, 09 Aug 2018 08:42:46 GMT - View all Chicago, IL jobs
          Data Science Lead - Johnson & Johnson Family of Companies - Pennsylvania      Cache   Translate Page      
Develop and deploy machine learning and predictive modeling solutions to drive business improvement in manufacturing and supply chain....
From Johnson & Johnson Family of Companies - Thu, 30 Aug 2018 01:47:11 GMT - View all Pennsylvania jobs
          Data Science Lead - Johnson & Johnson Family of Companies - Raritan, NJ      Cache   Translate Page      
Develop and deploy machine learning and predictive modeling solutions to drive business improvement in manufacturing and supply chain....
From Johnson & Johnson Family of Companies - Thu, 30 Aug 2018 01:47:11 GMT - View all Raritan, NJ jobs
          Data Scientist (Commercial Analytics) - Terex Corporation - Redmond, WA      Cache   Translate Page      
Experience in working with multiple ERP systems, in particular Oracle. Must have a proven ability to drive business results with their analytical insights....
From Terex Corporation - Mon, 10 Sep 2018 16:12:27 GMT - View all Redmond, WA jobs
          Sr Data Scientist - T-Mobile - Bellevue, WA      Cache   Translate Page      
This position will solve business problems and explore business questions through applied statistics and/or data mining techniques....
From T-Mobile - Wed, 29 Aug 2018 05:15:00 GMT - View all Bellevue, WA jobs
          Scientifique des Données Senior / Senior Data Scientist - OSS Business Exploration - Ericsson - Montréal, QC      Cache   Translate Page      
The Montreal Business Area Digital Services (BDGS) Solution Area (SA) Operations and Support Subsystem (OSS) Business Exploration team proposes and executes on...
From Ericsson - Mon, 20 Aug 2018 15:18:09 GMT - View all Montréal, QC jobs
          Senior Data Scientist - Prudential - Newark, NJ      Cache   Translate Page      
Identify analytical solutions for business problems. And a passion for generating business impact. Develop and maintain consultative relationships with key...
From Prudential - Wed, 11 Jul 2018 21:51:46 GMT - View all Newark, NJ jobs
          Data Scientist - Prudential - Newark, NJ      Cache   Translate Page      
And a passion for generating business impact. Develop and maintain consultative relationships with key business stakeholders....
From Prudential - Wed, 11 Jul 2018 21:49:38 GMT - View all Newark, NJ jobs
          Staff Data Scientist - Intuit - Mountain View, CA      Cache   Translate Page      
At least 5 years’ experience in applying Machine Learning techniques to solve business problems. As a member of the Intuit data science community, present in...
From Intuit - Tue, 11 Sep 2018 21:55:51 GMT - View all Mountain View, CA jobs
          Principal Data Scientist - Intuit - Mountain View, CA      Cache   Translate Page      
At least 8 years’ experience in applying Machine Learning techniques to solve business problems. As a member of the Intuit data science community, present in...
From Intuit - Tue, 07 Aug 2018 00:30:31 GMT - View all Mountain View, CA jobs
          Data Scientist - eBay Inc. - Austin, TX      Cache   Translate Page      
GBM, logistic regression, clustering, neural networks, NLP Strong analytical skills with good problem solving ability eBay is a Subsidiary of eBay....
From eBay Inc. - Sat, 25 Aug 2018 08:05:35 GMT - View all Austin, TX jobs
          (USA-NY-New York) Data Science Intern      Cache   Translate Page      
*The Team:* The Data science team is a newly formed applied research team within S&P Global Ratings that will be responsible for building and executing a bold vision around using Machine Learning, Natural Language Processing, Data Science, knowledge engineering, and human computer interfaces for augmenting various business processes. *The Impact:* This role will have a significant impact on the success of our data science projects ranging from choosing which projects should be undertaken, to delivering highest quality solution, ultimately enabling our business processes and products with AI and Data Science solutions. *What s in it for you:* This is a high visibility team with an opportunity to make a very meaningful impact on the future direction of the company. You will work with other highly accomplished team members to * Implement data science algorithms * Solve business problems using data science methods * Collaborate effectively with technical and non-technical partners * Create state of the art Augmented Intelligence, Data Science and Machine Learning solutions. * Assist in tracking quantitative and qualitative metrics to measure process and - or content. Provides fact-based interpretation and analysis of findings. *Responsibilities:* As an intern you will be responsible for building AI and Data Science models. You will need to rapidly prototype various algorithmic implementations and test their efficacy using appropriate experimental design and hypothesis validation. *Basic Qualifications:* * Currently enrolled in PhD or MS in Computer Science, Computational Linguistics, Artificial Intelligence, Statistics, or related field. * Strong ability to code in Python or Java *Preferred Qualifications:* * Experience with Financial data sets, or S&P s credit ratings process is highly preferred. * Knowledge and working experience in one or more of the following areas:* Natural Language Processing, Machine Learning, Question Answering, Text Mining, Information Retrieval, Distributional Semantics, Data Science, Knowledge Engineering *To all recruitment agencies:* S&P Global does not accept unsolicited agency resumes. Please do not forward such resumes to any S&P Global employee, office location or website. S&P Global will not be responsible for any fees related to such resumes. S&P Global is an equal opportunity employer committed to making all employment decisions without regard to race - ethnicity, gender, pregnancy, gender identity or expression, color, creed, religion, national origin, age, disability, marital status (including domestic partnerships and civil unions), sexual orientation, military veteran status, unemployment status, or any other basis prohibited by federal, state or local law. Only electronic job submissions will be considered for employment. *If you need an accommodation during the application process due to a disability, please send an email to:* EEO.Compliance@spglobal.com and your request will be forwarded to the appropriate person. The EEO is the Law Poster http://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf describes discrimination protections under federal law.
          (DEU-Munich) Data Engineer – Innovation Scaling for Web & Mobile Applications      Cache   Translate Page      
*Role Title:* Data Engineer Innovation Scaling for Web & Mobile Applications - 10A *The Role:* You live to break down and solve complex problems by creating practical, maintainable, and scalable solutions. You're a great person that willingly collaborates, listens and cares about your peers. If this is you then you have the best premises to join our team. In your role as the Data Engineer you will be responsible for the end to end Data Migration development, ownership and management. Our department is mainly responsible for the transition and scaling of the prototypes, generated by the innovation department, towards a fully integrated solution which our customers can rely on. Besides that we are also responsible for the enhancements & maintenance of existing products. *Your responsibilities will include but are not limited to:* * Build the infrastructure required for optimal extraction, transformation and loading of data from a wide variety of data sources incl. using SQL, Hadoop and AWS data sources. Document & consolidate data sources if required. * Collaborate with local development & data teams and the central data management group * Identify, design and implement internal process improvements:* automating manual processes, optimizing data delivery, re-design infrastructure for greater scalability etc. * Enable cutting-edge customer solutions by retrieving and aggregating data from multiple sources and compiling it into digestible and actionable forms. * Act as a trusted technical advisor for the teams and stakeholders. * Work with managers, software developers, and scientists to design and develop data infrastructure and cutting-edge market solutions. * Create data tools for analytics and data science team members tat assist them in building and optimizing our products into innovative business leaders in their segment. * Derive Unsupervised and Supervised Insights from Data with below specializations * Provide Machine Learning competences o Working on various kind of data like Continuous Numerical, Discrete, Textual, Image, Speech, Baskets etc. o Experience in Data Visualization, Predictive Analytics, Machine Learning, Deep Learning, Optimization etc. o Derive and Drive business Metrics and Measurement Systems to enable for AI readiness. o Handle large datasets using big data technologies. *The Impact:* You have the opportunity to shape one of the oldest existing industries in one of the largest enterprises in the market. Through active participation in shaping and improving our ways to achieve technical excellence you will drive and improve our business. *The Career Opportunity:* You will be working within flat hierarchies in a young and dynamic team with flexible working hours. You will benefit from a bandwidth of career enhancing opportunities. You have very good opportunities to shape your own working environment in combination with a very good compensation as well as benefits and will experience the advantage of both a big enterprise and a small start-up at the same time. Since the team is fairly small you will benefit from high trust and responsibility given to you. Also you will be a key person to grow our team. You should also be motivated to introduce new innovative processes and tools into an existing global enterprise structure. *The Team - The Business:* We are a small, highly motivated team in a newly set up division to scale innovation. We use agile methodologies to drive performance and we share and transfer knowledge as well as embracing methods such as pairing or lightning talks to do so. We are always trying to stay ahead of things and try to be state-of-the-art and cutting-edge. *Knowledge & Skills:* * Proven experience in a data engineering, business analytics, business intelligence or comparable data engineering role, including data warehousing and business intelligence tools, techniques and technology * B.S. degree in math, statistics, computer science or equivalent technical field * Experience transforming raw data into information. Implemented data quality rules to ensure accurate, complete, timely data that is consistent across databases. * Demonstrated ability to think strategically about business, product, and technical challenges * Experience in data migrations and transformational projects * Fluent English written and verbal communication skills * Effective problem-solving and analytical capabilities * Ability to handle a high pressure environment * Programming & Tool skills, Python, Spark, Tableau, XLMiner, Linear Regression, Logistic Regression, Unsupervised Machine Learning, Supervised Machine Learning, Forecasting, Marketing, Pricing, SCM, SMAC Analytics *_Beneficial experience:* _ * Experience in NoSQL databases (e.g. Dynamo DB, Mongo DB) * Experience in RDBMS databases (e.g. Oracle DB) *_About Platts and S&P Global_* *Platts is a premier source of benchmark price assessments and commodities intelligence. At Platts, the content you generate and the relationships you build are essential to the energy, petrochemicals, metals and agricultural markets. Learn more at https:* - - www.platts.com - *S&P Global*includes Ratings, Market Intelligence, S&P Dow Jones Indices and Platts. Together, we re the foremost providers of essential intelligence for the capital and commodities markets. - S&P Global is an equal opportunity employer committed to making all employment decisions without regard to race - ethnicity, gender, pregnancy, gender identity or expression, colour, creed, religion, national origin, age, disability, marital status (including domestic partnerships and civil unions), sexual orientation, military veteran status, unemployment status, or other legally protected categories, subject to applicable law. - *To all recruitment agencies:* S&P Global does not accept unsolicited agency resumes. Please do not forward such resumes to any S&P Global employee, office location or website. S&P Global will not be responsible for any fees related such resumes.
          (USA-NY-New York) Data Scientist      Cache   Translate Page      
*The Team:* The Data science team is a newly formed applied research team within S&P Global Ratings that will be responsible for building and executing a bold vision around using Machine Learning, Natural Language Processing, Data Science, knowledge engineering, and human computer interfaces for augmenting various business processes. *The Impact:* This role will have a significant impact on the success of our data science projects ranging from choosing which projects should be undertaken, to delivering highest quality solution, ultimately enabling our business processes and products with AI and Data Science solutions. *What s in it for you:* This is a high visibility team with an opportunity to make a very meaningful impact on the future direction of the company. You will work with senior leaders in the organization to help define, build, and transform our business. You will work closely with other senior scientists to create state of the art Augmented Intelligence, Data Science and Machine Learning solutions. *Responsibilities:* As a Data Scientist you will be responsible for building AI and Data Science models. You will need to rapidly prototype various algorithmic implementations and test their efficacy using appropriate experimental design and hypothesis validation. *Basic Qualifications:* BS in Computer Science, Computational Linguistics, Artificial Intelligence, Statistics, or related field with 5 years of relevant industry experience. *Preferred Qualifications:* * MS in Computer Science, Statistics, Computational Linguistics, Artificial Intelligence or related field with 3 years of relevant industry experience. * Experience with Financial data sets, or S&P s credit ratings process is highly preferred. * Knowledge and working experience in one or more of the following areas:* Natural Language Processing, Machine Learning, Question Answering, Text Mining, Information Retrieval, Distributional Semantics, Data Science, Knowledge Engineering * Proficient programming skills in a high-level language (e.g. Java, Scala, Python, C - C , Perl, Matlab, R) * Experience with statistical data analysis, experimental design, and hypotheses validation * Project-based experience with some of the following tools:* * Applied machine learning (e.g. libSVM, Shogun, Scikit-learn or similar) * Natural Language Processing (e.g., ClearTK, ScalaNLP - Breeze, ClearNLP, OpenNLP, NLTK, or similar) * Statistical data analysis and experimental design (e.g., using R, Matlab, iPython, etc.) * Information retrieval and search engines, e.g. Solr - Lucene * Distributed computing platforms, such as Hadoop (Hive, HBase, Pig), Spark, GraphLab * Databases (traditional and noSQL) *At S&P Global, we don t give you intelligencewe give you essential intelligence. The essential intelligence you need to make decisions with conviction. We re the world s foremost provider of ratings, benchmarks and analytics in the global capital and commodity markets. Our divisions include:* * S&P Global Ratings, which provides credit ratings, research and insights essential to driving growth and transparency. * S&P Global Market Intelligence, which provides insights into companies, markets and data so that business and financial decisions can be made with conviction. * S&P Dow Jones Indices, the world s largest resource for iconic and innovative indices, which helps investors pinpoint global opportunities. * S&P Global Platts, which equips customers to identify and seize opportunities in energy and commodities, stimulating business growth and market transparency. *To all recruitment agencies:* S&P Global does not accept unsolicited agency resumes. Please do not forward such resumes to any S&P Global employee, office location or website. S&P Global will not be responsible for any fees related to such resumes. S&P Global is an equal opportunity employer committed to making all employment decisions without regard to race - ethnicity, gender, pregnancy, gender identity or expression, color, creed, religion, national origin, age, disability, marital status (including domestic partnerships and civil unions), sexual orientation, military veteran status, unemployment status, or any other basis prohibited by federal, state or local law. Only electronic job submissions will be considered for employment. *If you need an accommodation during the application process due to a disability, please send an email to:* EEO.Compliance@spglobal.com and your request will be forwarded to the appropriate person. The EEO is the Law Poster http://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf describes discrimination protections under federal law.
          (USA-NY-New York) Engineering Director, Data Science      Cache   Translate Page      
*The Team:* The Data science team is a newly formed applied research team within S&P Global Ratings that will be responsible for building and executing a bold vision around using Machine Learning, Natural Language Processing, Data Science, knowledge engineering, and human computer interfaces for augmenting various business processes. *The Impact:* This role will have a significant impact on the success of our data science projects ranging from choosing which projects should be undertaken, to delivering highest quality solution, ultimately enabling our business processes and products with AI and Data Science solutions. *What s in it for you:* This is a high visibility leadership role with an opportunity to make meaningful impact on the future direction of the company. You will define new opportunities for business impact and will work closely with other senior leaders to work on the entire pipeline from ideation to production to user adoption. *Responsibilities:* As a Director you will be the technical lead on projects and be responsible for solution design and implementation. You will need to work closely with internal stakeholders and users, mentor junior scientists, and identify opportunities that lead to business impact and ultimately drive the Data Science vision. You will be responsible for determining the algorithmic implementation best suited for a use case, and the experimental design for testing its efficacy. *Basic Qualifications:* MS in Computer Science, Computational Linguistics, Artificial Intelligence or related field with 10 years of relevant industry experience *Preferred Qualifications:* * Degree in Computer Science, Computational Linguistics, Artificial Intelligence or related field with 7 years of relevant industry experience * Experience in mentoring or managing junior scientists and engineers, working with business stakeholders and users, providing research direction and solution design * Knowledge and working experience in one or more of the following areas:* Natural Language Processing, Machine Learning, Question Answering, Text Mining, Information Retrieval, Distributional Semantics, Data Science, Knowledge Engineering * Proficient programming skills in a high-level language (e.g. Java, Scala, Python, C - C , Perl, Matlab, R) * Experience with statistical data analysis, experimental design, and hypotheses validation * Project-based experience with some of the following tools:* * Applied machine learning (e.g. libSVM, Shogun, Scikit-learn or similar) * Natural Language Processing (e.g., ClearTK, ScalaNLP - Breeze, ClearNLP, OpenNLP, NLTK, or similar) * Statistical data analysis and experimental design (e.g., using R, Matlab, iPython, etc.) * Information retrieval and search engines, e.g. Solr - Lucene * Distributed computing platforms, such as Hadoop (Hive, HBase, Pig), Spark, GraphLab * Databases (traditional and noSQL)
          (USA-TX-Arlington) Data Warehouse Administrator II      Cache   Translate Page      
**Job Description** Texas Health Resources is seeking a Data Warehouse Administrator II, Arlington, TX Full Time: 1 st Shift 40 hours Monday-Friday, hours may vary LOCATION: Texas Health Resources 612 E. Lamar Blvd. Arlington, TX 76011 SALARY: Commensurate with experience $39.55 - $58.97 per hour Position Summary: Supports the Data Integration team’s effort to acquire and integrate new source system data into a data lake as part of THR’s extended data warehouse strategy supporting THR’s Vision 2026 strategic initiatives. Provides support to data analysts and data scientists in staging and preparing the data to support data mining and analysis to gain insights and rapidly respond to new business opportunities. Administers analytic tools and environment to support data analysis efforts provides hands on application support for data analysts and data scientists as needed. Essential Functions: New Development With minimal guidance, work with business sponsors, SMES and application teams to gather and document business requirements, analyze and assess availability, quality and data lineage of source system data. Design analytic solutions that meet business needs. Collaborate with business, analysts and SMEs to rapidly develop and refine business intelligence reporting solutions using SQL, Tableau, SPSS or other technologies. If necessary, develop interim data integration processes using SQL, Alteryx, Data Stage or other data integration technologies. Support & Maintenance Support production BI applications to manage source system changes, optimize application and ensure availability, accuracy, security and acceptable performance. Administration Collaborate with business, analysts, EDW team, application teams and other stakeholders to design, develop, test and implement production Business Intelligence solutions that are fully integrated into the Enterprise Data Warehouse. Process Comply with THR information security and privacy policies, ITSM processes and Data Integration development and design standards and best practices. Follow data governance requirements and documentation including but not limited to business definitions, technical definition, and may function as data SME. Skills, Growth, Mentoring, Training, Knowledge Mgmt Develops interrelationships among partners and customers and analysts who have similar information needs and manages those relationships to assure effective solutions. Mentor and train business stakeholders, analysts and information consumers on data, business intelligence-related technology and information assets. **Qualifications** Bachelor's Degree: Computer Science, Engineering, Computer Engineering, or relevant field; or 4 years relevant experience in lieu of a degree. Experience: 2 Years with a Bachelor's degree - Hands-on experience with these technologies: Microsoft Integration Platforms, Interface Engine Utilities, SFTP / Scripting. Skills and abilities: Or 6 Years with no degree - Hands-on experience with these technologies: Microsoft Integration Platforms, Interface Engine Utilities, SFTP / Scripting 6 Preferred Skills & Abilities: + Communication and presentation skills and a proven ability to work effectively with employees with various levels of technical aptitude while demonstrating a strong understanding of the business problems they are trying to solve. + Proficient in SQL development with experience on more than one database platform, such as Netezza, Oracle, Teradata and/or MS SQL, strongly preferred. Teradata SQL development experience preferred + Experience with ETL tools such as Data Stage or SSIS or data mining and analysis tools such as Alteryx or SPSS Modeler preferred. + Experience delivering solutions using agile and/or rapid application development methods preferred. + Demonstrated ability to take on at least three projects concurrently and manage changes in scope along the way preferred. **Entity Information:** Texas Health Resources is one of the largest faith-based, nonprofit health care delivery systems in the United States and the largest in North Texas in terms of patients served. Texas Health has 25 acute-care and short-stay hospitals that are owned, operated, joint-ventured or affiliated with the system. It has more than 3,800 licensed beds, more than 21,100 employees of fully-owned/operated facilities plus 1,400 employees of consolidated joint ventures, and counts more than 5,500 physicians with active staff privileges at its hospitals. At Texas Health, we strive to create an atmosphere of respect, integrity, compassion and excellence for all who come in contact with us, be they patients or our employees. We are committed to diversity in our workforce, and our mission to serve spreads across ethnic, cultural, economic and generational boundaries. We invite you to join us in furthering your career through our accomplishments and philosophy of excellence. Employment opportunities are only reflective of wholly owned Texas Health Resources entities. We are an Equal Opportunity Employer and do not discriminate against any employees or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.
          (USA-GA-Atlanta) Sr. Data Science Analyst      Cache   Translate Page      
Req ID: W458159 Job Description Performs sophisticated data analytics (encompassing data mining, inferential statistical analysis, and predictive analytics, for example). Identifies actionable insights from various (or multiple) sources of data that measurably improve business outcomes or reduce business risk. Collects and prepares data for analysis, performs exploratory to advanced predictive and/or modeling analytics, and identifies data relationships (patterns and trends). Provides consultation to business leaders and other stakeholders on how to leverage analytic insights to build actionable strategies. Job Level: Experienced-level professional job that applies a solid understanding of concepts within own professional discipline and uses knowledge of the business and key processes gained practical experience. Solves routine problems of moderate complexity by analyzing possible solutions using experience, judgment and precedents. Focuses on enhancing knowledge of SunTrust's processes, culture and clients. Impacts quality of own work and the work of others on the team. Actively participates in projects, including planning and execution activities; may be responsible for a project workstream from start to finish. Provides informal guidance to new teammates. Works under moderate supervision. Qualifications Basic Qualfications: * Bachelor's degree or equivalent and 2 years of related experience or an equivalent combination of education and experience. * Solid understanding of principles, practices, theories, and/or methodologies associated with the professional discipline (e.g., information technology, project management, finance, risk management, etc.). * Ability to manage competing priorities. * Ability to solve problems in straightforward situations by analyzing possible solutions using experience, judgment and precedents. * Awareness of industry competitive landscape and the factors that differentiate SunTrust and other banks in the market. * Ability to communicate complex information in straightforward situations. Preferred Qualifications: * Master's degree or MBA and 3 years of related experience. * Previous experience in the banking industry. Equal Opportunity Employer: SunTrust supports a diverse workforce and is a Drug Testing and Equal Opportunity Employer. SunTrust does not discriminate against individuals on the basis of race, creed, color, gender, religion, national origin, age, disability, veteran status, pregnancy, marital status, citizenship status, sexual orientation, gender identity, genetic information, or any other classification protected by applicable laws. To review the EEO Poster, copy and paste the following link into your browser: http://www1.eeoc.gov/employers/upload/eeoc_self_print_poster.pdf http://www.dol.gov/ofccp/regs/compliance/posters/pdf/OFCCP_EEO_Supplement_Final_JRF_QA_508c.pdf © 2017 SunTrust Banks, Inc. All rights reserved. SunTrust is federally registered service marks of SunTrust Banks, Inc.
          (USA-GA-Atlanta) Data Scientist      Cache   Translate Page      
Req ID: W458103 Job Description Performs sophisticated data analytics (encompassing data mining, inferential statistical analysis, and predictive analytics, for example). Identifies actionable insights from various (or multiple) sources of data that measurably improve business outcomes or reduce business risk. Collects and prepares data for analysis, performs exploratory to advanced predictive and/or modeling analytics, and identifies data relationships (patterns and trends). Provides consultation to business leaders and other stakeholders on how to leverage analytic insights to build actionable strategies. Job Level: Experienced-level professional job that applies a solid understanding of concepts within own professional discipline and uses knowledge of the business and key processes gained practical experience. Solves routine problems of moderate complexity by analyzing possible solutions using experience, judgment and precedents. Focuses on enhancing knowledge of SunTrust's processes, culture and clients. Impacts quality of own work and the work of others on the team. Actively participates in projects, including planning and execution activities; may be responsible for a project workstream from start to finish. Provides informal guidance to new teammates. Works under moderate supervision. Qualifications Basic Qualfications: * Minimum Requirements:Bachelor's degree or equivalent and 2 years of related experience or an equivalent combination of education and experience. Solid understanding of principles, practices, theories, and/or methodologies associated with the professional discipline (e.g., information technology, project management, finance, risk management, etc.). Ability to manage competing priorities. Ability to solve problems in straightforward situations by analyzing possible solutions using experience, judgment and precedents. Awareness of industry competitive landscape and the factors that differentiate SunTrust and other banks in the market. Ability to communicate complex information in straightforward situations. * * Preferred Requirements:Master's degree or MBA and 3 years of related experience. Previous experience in the banking industry. * Equal Opportunity Employer: SunTrust supports a diverse workforce and is a Drug Testing and Equal Opportunity Employer. SunTrust does not discriminate against individuals on the basis of race, creed, color, gender, religion, national origin, age, disability, veteran status, pregnancy, marital status, citizenship status, sexual orientation, gender identity, genetic information, or any other classification protected by applicable laws. To review the EEO Poster, copy and paste the following link into your browser: http://www1.eeoc.gov/employers/upload/eeoc_self_print_poster.pdf http://www.dol.gov/ofccp/regs/compliance/posters/pdf/OFCCP_EEO_Supplement_Final_JRF_QA_508c.pdf © 2017 SunTrust Banks, Inc. All rights reserved. SunTrust is federally registered service marks of SunTrust Banks, Inc.
          (USA-GA-Atlanta) MGR ONLINE PRODUCT MANAGEMENT      Cache   Translate Page      
POSITION PURPOSE We are looking for an innovative, results-oriented, customer-centric and seasoned Senior Manager, to lead our Category Experience teams to define, drive and monitor the business on all aspects of demand, customer experience, and conversion. The Senior Manager should be passionate about the customer and the E-commerce industry and have proven results leading a team in a fast-paced environment. The successful candidate will lead a team that will evaluate and drive improvements in the overall look, feel, and navigation of customer-facing site aspects for their product areas on Homedepot.com. The Senior Manager will own their strategic vision and product roadmap for areas of Category digital experience. The candidate will manage a team of product managers and analysts in defining innovative customer experiences, coordinating with engineering and data science teams to ensure requirements are implemented effectively, and partnering as needed with internal and external business teams to guarantee successful prototyping and product implementations. The candidate will also need to roll up their sleeves as necessary to ensure projects stay on schedule and that the work product meets our high-quality bar. Importantly, the candidate must be able to work at the strategic level (generating bold and innovative ideas for growth) and at the tactical level (solving customer pain points). The successful Senior Manager, Category Experience must be able to: 25% - Recruit, hire and manage a team in defining a world-class customer experience that bridges online and multi-channel (in-store, mobile, call center, tablet, etc.); Gain a thorough understanding of customer needs, both existing and potential, and use that knowledge as thought leader and customer champion to create product roadmaps with their team which deliver site features that provide Home Depot customers with an unparalleled shopping experience. Inspire and motivate the team; Lead by example and reinforce best practices 25% - Maintain product and feature roadmap and manage prioritization and trade-offs among customer experience, site performance, and operational support load. As the subject matter expert, forecast, monitor, understand and report on team’s product lines and “own” the overall results 15% - Create buy-in for their product vision both within the online team and with key enterprise stakeholders. Lead cross-functionally to ensure Home Depot implements the business vision efficiently. Ensure team writes complete and detail-oriented product stories and requirements; Ensure clear communications of those requirements to the business, design, usability and development teams 15% Proactively identify and resolve strategic issues that may impair the team’s ability to meet strategic, financial, and technical goals 10% - Leverage their thorough understanding of the E-commerce industry, its seasonality and global business trends/events, and competitor/industry developments when managing priorities and trade-offs on their product and feature roadmap 10% Identify and assess the value of technology licensing with new vendors to deliver on innovative customer experience features NATURE AND SCOPE Position typically reports to Dir, Online Product Management. Accountable for direct supervision of the work activities of others. Planning, monitoring and reviewing work of subordinates is required. This may include direct supervision of a shift or the coordination of multiple work groups. Makes recommendations concerning selection, termination, performance appraisal and professional development. ENVIRONMENTAL JOB REQUIREMENTS Environment: Typically located in a comfortable indoor area. There may be regular exposure to mild physical discomfort from factors such as dust, fumes or odors, temperature extremes, loud noise, strong drafts, or bright lights. Travel: Typically requires overnight travel less than 10% of the time. Additional Environmental Job Requirements: MINIMUM QUALIFICATIONS Must be eighteen years of age or older. Must be legally permitted to work in the United States. Additional Minimum Qualifications: Education Required: The knowledge, skills and abilities typically acquired through the completion of a bachelor's degree program or equivalent degree in a field of study related to the job. Years of Relevant Work Experience: 5 years Physical Requirements: Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions, there may be a need to move or lift light articles. Preferred Qualifications: + MBA or MS-CIS preferred + Prior experience leading customer-facing experience teams + Demonstrated success building buy-in for an innovative and bold vision + Technical fluency; comfort understanding and discussing architectural concepts, schedule tradeoffs and new opportunities with technical team members + Nimbleness and comfort with ambiguity; comfort responding quickly to rapidly evolving threats and opportunities + Strong bias for action; ability to juggle multiple priorities and create a sense of urgency in a fast-paced, dynamic environment + A strong background (8+ years) in product management or marketing, preferably within a consumer goods, retail or internet company Knowledge, Abilities, Skills + Demonstrated ability to build and manage high-performance product management and prototyping teams + Strong strategic aptitude; proven ability to define a winning business strategy and product roadmap + Excellent customer experience intuition; demonstrated success in creating innovative and user-friendly websites and customer-facing features + Demonstrated ability to manage bottlenecks, provide escalation management, anticipate and make tradeoffs, balance the business needs versus technical constraints, and maximize business benefit and build great customer experience + Proven analytical and quantitative skills (experience with excel and access); ability to use hard data and metrics to back up assumptions and feature concepts; comfort with pro-forma financial and operational analysis + Implementation orientation; demonstrated ability to translate strategic differentiators into innovative and detailed product requirements + Demonstrated ability to manage multiple projects across diverse groups, including data science, experience product managers, engineers and QA - work prioritization and planning + Exceptional interpersonal, communication, cross-collaboration and team skills + Knowledge of E-commerce business KPIs and technologies + Ability to deliver initiatives from conception through completion + Proven ability to make smart feature (customer experience) versus time-to-market trade-offs + Street smarts and willingness to roll up your sleeve and do what’s necessary We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.
          Oracle DBA - Paychex Inc. - Webster, NY      Cache   Translate Page      
Bachelor's Degree in Computer Science or related discipline . Knowledge in the field of Data Management, Data Warehousing, Data Science, Reporting and Analytics...
From Paychex Inc. - Fri, 06 Jul 2018 12:48:34 GMT - View all Webster, NY jobs
          DEV OPS ARCHITECT -CONTRACT JOB - Zentech - Webster, NY      Cache   Translate Page      
Bachelors Degree in Computer Science,. Experience with Microsoft business intelligence/data science technologies is a plus (e.g.... $71 an hour
From Indeed - Wed, 29 Aug 2018 14:41:03 GMT - View all Webster, NY jobs
          Senior Data Scientist Consultant - GI Research - Milano, Lombardia      Cache   Translate Page      
The successful candidate will be a valid partner of the Business Developer Data Services contributing and developing the current retail and supplier client...
Da GI Research - Thu, 23 Aug 2018 17:20:19 GMT - Visualizza tutte le offerte di lavoro a Milano, Lombardia
          SAP’s cloud analytics update offers insights in seconds, not months      Cache   Translate Page      

With a refresh of its cloud analytics tools, SAP hopes to bring users new insights into their data more quickly and take some of the workload off the IT department.

It’s not that the previous analytics tools were all that slow; it’s just that to get the most out of some of their features, you needed a team of data scientists to build the right reports.

Now the company is using machine learning to help SAP Analytics Cloud users zoom in on key data correlations, according to Mike Flannagan, SAP’s senior vice president of analytics.

To read this article in full, please click here


          Extreme Learning Machines (ELMs) within Artificial Intelligence (AI)      Cache   Translate Page      

Learn all about the Extreme Learning Machine (ELM) Artificial Neural Network (ANN), and how you can best leverage it within Artificial Intelligence (AI) and data science projects including within regression, alternate feature generation, and classification. We cover the ELM architecture along with the ELM packages available in Julia and Python. We’ll conclude by comparing ELMs to multilayer perceptrons (MLPs).


          The Business Aspect of Artificial Intelligence (AI) in Data Science      Cache   Translate Page      

How can our businesses best benefit from Artificial Intelligence (AI)? We cover the relevant technologies necessary to make the most out of AI, such as GPUs to enable deep learning networks, quantum computing, and the cloud. We explore which industries and applications benefit most from AI, such as finance and retail. We share the resources required for AI projects, including computing, data, and educational resources. Know the investment required to incorporate AI into data science projects so that you can best leverage this technology.


          Search/Relevance - Staff Data Scientist - Walmart eCommerce - Sunnyvale, CA      Cache   Translate Page      
Design and implement state of the art Machine Learning approaches. With the help of some of the brightest minds in technology, merchandising, marketing, supply...
From Walmart eCommerce - Thu, 13 Sep 2018 09:14:57 GMT - View all Sunnyvale, CA jobs
          Senior Product Manager - Pinpoint Predictive, Inc - San Mateo, CA      Cache   Translate Page      
Launching new products in collaboration with data science, engineering, design, data visualization, and marketing....
From Pinpoint Predictive, Inc - Thu, 23 Aug 2018 14:12:38 GMT - View all San Mateo, CA jobs
          Data Scientist - Canadian National Railway - Montréal, QC      Cache   Translate Page      
IVADO, Vector Institute, Scale.AI) to design and implement applied AI/data science models that solve real world problems. Why do you want this job?...
From Canadian National Railway - Tue, 11 Sep 2018 07:15:16 GMT - View all Montréal, QC jobs
          Sr. Data Scientist - Shutterstock - Montréal, QC      Cache   Translate Page      
Role: Shutterstock is looking for an experienced data scientist to join our world-class artificial intelligence team! The Artificial Intelligence team's...
From Shutterstock - Mon, 10 Sep 2018 16:21:19 GMT - View all Montréal, QC jobs
          Data Scientist - FedEx Services - Brookfield, WI      Cache   Translate Page      
Women’s Business Enterprise National Council “America’s Top Corporations for Women’s Business Enterprises” - 2016....
From FedEx - Wed, 29 Aug 2018 00:08:19 GMT - View all Brookfield, WI jobs
          Principal Technologist - Machine Learning and Data Science - Blue Origin - Kent, WA      Cache   Translate Page      
While in this role, you will leverage your extensive experience in machine learning and data science to accelerate and innovate across business areas to drive...
From Blue Origin - Wed, 13 Jun 2018 05:31:45 GMT - View all Kent, WA jobs
          Data Scientist in Food Animal Biology and Production Systems - University of Minnesota - Minneapolis-Saint Paul, MN      Cache   Translate Page      
Design and assist in the development of apps with easy to use interfaces that aggregate data from micro to macro scales for decision-making involving animal...
From University of Minnesota - Thu, 12 Jul 2018 23:47:18 GMT - View all Minneapolis-Saint Paul, MN jobs
          Data Scientist - eBay Inc. - Austin, TX      Cache   Translate Page      
GBM, logistic regression, clustering, neural networks, NLP Strong analytical skills with good problem solving ability eBay is a Subsidiary of eBay....
From eBay Inc. - Sat, 25 Aug 2018 08:05:35 GMT - View all Austin, TX jobs
          Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Knowledge and experience on applying statistical and machine learning techniques on real business data....
From Lincoln Financial Group - Mon, 27 Aug 2018 18:47:26 GMT - View all Boston, MA jobs
          Sr. Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Implements and maintains predictive and statistical models to identify business opportunities and solve complex business problems....
From Lincoln Financial Group - Fri, 17 Aug 2018 18:34:07 GMT - View all Boston, MA jobs
          Como vender um carro usado por valor acima da tabela FIPE      Cache   Translate Page      
Existem muitos critérios a serem considerados na hora de precificar um carro usado, e um deles é não usar a tabela FIPE (Foto: Katemangostar/Freepik.com)

 

A grande dúvida ao vender o carro usado é: por quanto vender? De certa forma, as plataformas virtuais amenizaram o problema em um primeiro momento, mas voltaram a provocar disparidade de valores justamente por facilitar a busca dos interessados.

Um ponto de partida óbvio é apelar para a tabela FIPE. No entanto, essa referência deixa de lado muitos fatores importantes para precificar o SEU carro. A razão é simples: os valores listados pela Fundação Instituto de Pesquisas Econômicas reflete os preços médios no mercado nacional. E não se trata de uma tabela oficial.

Porém, como a própria descrição oficial no site da FIPE define, "os preços efetivamente praticados variam em função da região, conservação, cor, acessórios ou qualquer outro fator que possa influenciar as condições de oferta e  por um veículo específico".

 

“A tabela não considera quilometragem, estado de conservação do carro e a instalação de acessórios”, diz Antônio Avellar, co-fundador da Volanty, uma plataforma online de compra e venda de veículos seminovos.

Na prática, o valor da tabela FIPE para dois carros iguais é o mesmo, mas se um deles tem algo de errado, como problemas estéticos ou mecânicos, dificilmente um comprador optaria pela opção em pior estado.

Ou seja, ele não vai pagar a mesma coisa que pagaria pelo mesmo carro que não apresenta nenhum risco.

Então, o primeiro passo é analisar como está o mercado para o modelo que você quer colocar à venda. Pesquise o seu carro em sites de classificados, procure por aqueles que tenham características parecidas, como modelo, versão, ano, cor, região e quilometragem. Estes são alguns dos critérios que devem ser levados em consideração na hora de precificar.

Existem quatro coisas que são fundamentais, de acordo com Henrique Cavalhieri, gerente de varejo da DEKRA Brasil: Estado de conservação, região, cor e histórico do veículo. Mas por quê?

Estado de conservação
Claro que o que mais chama a atenção do comprador é a estética do carro. “A maioria das pessoas que procuram um carro usado gostaria que ele tivesse cara de carro novo”, afirma Antônio Avellar.

Mas não é bem assim: o carro usado foi usado. E marcas de uso, ainda que mínimas, são inevitáveis. Nesse sentido, uma alternativa a considerar é fazer o reparo antes de vender. A gente fala sobre martelinho de ouro aqui.

Só que nem sempre vale a pena fazer esses reparos, pois o veículo pode não valorizar na mesma proporção dos gastos com o conserto.

Carro desvaloriza até 24% só de tirar da concessionária
É o que aponta um estudo realizado pela InstaCarro, uma plataforma online de venda de veículos. Portanto é essencial que a aparência seja a melhor possível, esteja bem conservado, sem arranhões, amassados ou problemas com a pintura.

Estética
O carro pertencia a fumante? Tem marcas de queimaduras nos bancos? O mais importante é eliminar o odor, mas se houver outras marcas de fumo no carro e afetar sua estrutura, a desvalorização é certa. “Se tiver algum sinal no interior do carro, um furo no banco, por exemplo, por causa de cigarro ou alguma ação oxidante, como um amarelado por causa de fumaça”, conta Sérgio Guidi, gerente de operações da InstaCarro.

Como valorizar a estética do carro? “Preservar a pintura, fazer polimento a cada seis meses para protegê-la, higienizar o carro e tomar outros cuidados básicos”, aconselha Sérgio Guidi.

Mas não é só a estética que define um bom estado de conservação. O motor (ou outras peças mecânicas) podem não ser evidentes para uma grande parcela de compradores que não têm conhecimento mecânico. Porém, as pesquisas indicam desvalorização média de 9% quando há problemas.

Região
Alguns veículos têm maior aceitação em determinadas regiões.
“No Mato Grosso do Sul e no Mato Grosso picapes de cabine dupla têm um valor de venda muito alto, porque são muito queridas pela população desses estados”, exemplifica Henrique Cavalhieri. Portanto é importante verificar como é a procura pelo modelo do seu carro por cidade ou estado.

Sabendo disso, você terá maior probabilidade de obter mais retorno sobre o preço do usado nessas regiões que privilegiam determinado produto.

O fato de um veículo ser do litoral, não necessariamente afetará seu preço. O principal indicador, segundo os especialistas, está no quanto a maresia afetou sua estrutura e aparência.

“Eu não posso desvalorizar pela maresia, mas há desvalorização se a pintura está afetada por isso ou se houver, embaixo do carro, alguma parte que está com risco de corrosão em função de um possível ambiente agressivo”, afirma Sérgio Guidi.

Há lojas que tendem a desvalorizar o carro segundo a placa. Ou seja, veículos cuja a licença tenha letras destinadas a uma determinada região pode influenciar o valor de forma negativa.

Cor
Este é um fator que pesa na hora de vender um carro. Henrique conta que “as cores preto, branco, prata e cinza representam quase 80% das vendas de carro zero km no Brasil”. Ou seja, outras cores podem ter uma aceitação menor e, consequentemente, desvalorização e menores chances de venda.

E o gosto pela cor também varia de região e modelo do veículo. “Têm algumas regiões do Brasil que preferem cores mais claras ou vivas e outras regiões preferem cores mais escuras ou sóbrias”, esclarece o gerente de varejo da DEKRA Brasil.

Segmentos considerados mais tradicionais, como o de sedãs médios ou SUVs, são ainda mais sensíveis. Por exemplo, vender um Corolla verde pode ter maior tempo de exposição no ponto de venda do que um veículo igual na cor prata ou preta.

Histórico do veículo
Esse fator considera alguma colisão, o que afeta a estrutura do carro, e quilometragem percorrida. Se a integridade do veículo estiver afetada, ele pode ser desvalorizado em até 8%. Além disso, a cada 10.000 km rodados, você perde 0,5% do valor.

O histórico também leva em consideração se o carro foi leiloado, sofreu algum roubo ou furto e se envolveu em algum sinistro. Neste caso, as perdas são de 9,2% para aqueles que vieram de leilão, 3,5% para os que já foram roubados/furtados e 6,9% se passou por sinistro.

 

Bônus
A marca do carro também acaba se tornando um fator de precificação. De acordo com o estudo da InstaCarro, as cinco marcas que vendem mais rápido são Honda, Hyundai, Toyota, Chevrolet e Ford. Nesta condição devem ser considerados também custo de manutenção e disponibilidade de peças para reposição.

Segundo Heblon Barbosa, gerente de Data Science da InstaCarros, veículos de algumas marcas não são fáceis de vender: “Peugeot e JAC possuem um mercado mais difícil, é mais difícil achar comprador para esses carros. Por isso acabam sendo desvalorizados”.

Portanto, o ideal é pesquisar o valor de mercado e colocar na ponta do lápis os fatores que desvalorizam o seu carro para, enfim, definir o preço do seu usado.

A lista abaixo mostra os dados obtidos no estudo completo realizado pela InstaCarro (dados de janeiro/2017 a dezembro/2017):

Que características colaboram para que o carro seja vendido mais rápido?
Marca
Quilometragem
Idade
Integridade da estrutura e do motor
Custo de manutenção e peças de reposição

Quais marcas vendem mais rápido?
Honda
Hyundai
Toyota
Chevrolet
Ford

Desvalorização do carro por ano
Ano do modelo/ Desvalorização (%)
2018:
-24%
2017: -28%
2016: -31%
2015: -41%
2014: -51%
2013: -60%
2012: -69%
2011: -76%

Desvalorização do carro por tipo de problema
Tipo de problema/Desvalorização (%)
Cada 10.000 km:
-0,5%
Motor: -9%
Estrutura: -8%

Desvalorização do carro pelo histórico
Acontecimento/Desvalorização (%)
Leilão:
-9,2%
Roubo/Furto: -3,5%
Sinistro: -6,9%

Probabilidade de desenvolver um problema de motor de acordo com a idade do carro
Idade (anos)/ Probabilidade (%)
1:
15%
2: 20%
3: 25%
4: 30%
5: 35%
6: 40%
7: 45%
8: 50%


          Data Scientist - Yamaha - Cypress, CA      Cache   Translate Page      
Develop statistical models, machine learning-based tools or processes to measure and manage business performance....
From Yamaha - Wed, 22 Aug 2018 00:54:18 GMT - View all Cypress, CA jobs
          Associate, ML Pipelines for AI Consultant - KPMG - Seattle, WA      Cache   Translate Page      
Broad, versatile knowledge of analytics and data science landscape, combined with strong business consulting acumen, enabling the identification, design and...
From KPMG LLP - Fri, 07 Sep 2018 02:02:14 GMT - View all Seattle, WA jobs
          Director, NLP Data Scientist - KPMG - Seattle, WA      Cache   Translate Page      
Educate internal and external clients through workshops, and present at conferences, seminars, courses, and training events....
From KPMG LLP - Fri, 07 Sep 2018 02:01:04 GMT - View all Seattle, WA jobs
          Data Scientist - Deloitte - Springfield, VA      Cache   Translate Page      
Demonstrated knowledge of machine learning techniques and algorithms. We believe that business has the power to inspire and transform....
From Deloitte - Fri, 10 Aug 2018 06:29:44 GMT - View all Springfield, VA jobs
          Associate, ML Pipelines for AI Consultant - KPMG - Dallas, TX      Cache   Translate Page      
Broad, versatile knowledge of analytics and data science landscape, combined with strong business consulting acumen, enabling the identification, design and...
From KPMG LLP - Fri, 07 Sep 2018 02:02:14 GMT - View all Dallas, TX jobs
          Principal Data Scientist - Clockwork Solutions - Austin, TX      Cache   Translate Page      
Support Clockwork’s Business Development efforts. Evaluates simulation analysis output to reveal key insights about unstructured, chaotic, real-world systems....
From Clockwork Solutions - Mon, 27 Aug 2018 10:03:12 GMT - View all Austin, TX jobs
          Lead Data Scientist - Clockwork Solutions - Austin, TX      Cache   Translate Page      
Support Clockwork’s Business Development efforts. Evaluates simulation analysis output to reveal key insights about unstructured, chaotic, real-world systems....
From Clockwork Solutions - Mon, 27 Aug 2018 10:03:12 GMT - View all Austin, TX jobs
          Director, NLP Data Scientist - KPMG - New York, NY      Cache   Translate Page      
Educate internal and external clients through workshops, and present at conferences, seminars, courses, and training events....
From KPMG LLP - Fri, 07 Sep 2018 02:01:04 GMT - View all New York, NY jobs
          Extreme Learning Machines (ELMs) within Artificial Intelligence (AI)      Cache   Translate Page      

Learn all about the Extreme Learning Machine (ELM) Artificial Neural Network (ANN), and how you can best leverage it within Artificial Intelligence (AI) and data science projects including within regression, alternate feature generation, and classification. We cover the ELM architecture along with the ELM packages available in Julia and Python. We’ll conclude by comparing ELMs to multilayer perceptrons (MLPs).


          The Business Aspect of Artificial Intelligence (AI) in Data Science      Cache   Translate Page      

How can our businesses best benefit from Artificial Intelligence (AI)? We cover the relevant technologies necessary to make the most out of AI, such as GPUs to enable deep learning networks, quantum computing, and the cloud. We explore which industries and applications benefit most from AI, such as finance and retail. We share the resources required for AI projects, including computing, data, and educational resources. Know the investment required to incorporate AI into data science projects so that you can best leverage this technology.


          SAP Analytics Cloud Helps Business Users Make Better Decisions with Augmented Analytics      Cache   Translate Page      

According to a new press release out of the company, “SAP SE today announced the SAP Analytics Cloud solution is now available with new machine learning features to uncover correlations in an organization’s data and help users make faster, more confident decisions… Data scientists, who use scientific methods to extract knowledge from data, are a […]

The post SAP Analytics Cloud Helps Business Users Make Better Decisions with Augmented Analytics appeared first on DATAVERSITY.


          Data Scientist - Game Hive - Toronto, ON      Cache   Translate Page      
Our most popular games include the Tap Titans, Beat the Boss and Antrim Escape series (and many, more!). Game Hive is building a new generation of casual mobile...
From Indeed - Wed, 15 Aug 2018 13:34:49 GMT - View all Toronto, ON jobs
          You Ask, I Answer: The Promise of AI and Data for Marketing      Cache   Translate Page      

Denis asks, “What is the big promise that AI holds when it comes to data? What types of solutions do you see emerging from this that will help marketers?” Look at the data science lifecycle. Every repeatable choice along this lifecycle has at least some portion which is a repetitive, predictable process. Where we’ll see […]

The post You Ask, I Answer: The Promise of AI and Data for Marketing appeared first on Christopher S. Penn Marketing Blog.


          Scientist, Data Science (1 of 2) - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Consideration will be given to Raritan, NJ; Janssen Research &amp; Development LLC, a Johnson &amp; Johnson company, is recruiting for a Scientist, Data Science....
From Johnson & Johnson Family of Companies - Wed, 05 Sep 2018 16:05:17 GMT - View all Spring House, PA jobs
          Senior Scientist, Data Science (1 of 2) - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Consideration will be given to Raritan, NJ; Janssen Research &amp; Development LLC, a Johnson &amp; Johnson company, is recruiting for a Senior Scientist, Data Science....
From Johnson & Johnson Family of Companies - Wed, 05 Sep 2018 16:05:17 GMT - View all Spring House, PA jobs
          Senior Analyst, Data Science (1 of 2) - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Consideration will be given to Raritan, NJ; Janssen Research &amp; Development LLC, a Johnson &amp; Johnson company, is recruiting for a Senior Analyst, Data Science....
From Johnson & Johnson Family of Companies - Wed, 05 Sep 2018 04:05:45 GMT - View all Spring House, PA jobs
          Data Scientist      Cache   Translate Page      
CA-San Diego, job summary: Job Summary The Data Scientist who is passionate about data and want to apply machine learning techniques to solve real-world problems for our clients. This person will be expected to be proficient in the exploration and understanding of structured and unstructured data, machine learning and data mining techniques, statistical modeling methods, time series, text mining, recommendation
          5 Ways Artificial Intelligence Is Already Transforming the Banking Industry      Cache   Translate Page      

Opinions expressed by Entrepreneur contributors are their own.

Artificial Intelligence (AI) -- and its growing impact on and applicability for individuals and businesses alike -- is one of today’s most widely discussed topics. From virtual assistants like Siri and Alexa,to chatbots created by Facebook and Drift , AI is having a significant impact on the lives of consumers.

Related:#Five Technology Trends That Will Disrupt Your Banking Style

A study from Statista showed that the number of consumers using virtual assistants worldwide is expected to exceed one billion in 2018. Additionally, a 2018 survey by Accenture projected that 37 percent of U.S. consumers will own a digital voice assistant (DVA) device by the end of 2018.

It is readily apparent how AI-powered technology is making inroads into everyday life through DVAs and other consumer products, but AI is also having a transformative effect on an industry that impacts virtually all consumers and businesses: banking. Here are five ways that AI is already transforming the banking industry.

Customer service automation

As natural language processing technology evolves, consumers find it increasingly difficult to distinguish between a voice bot and a human customer service representative. This stems from improved abilities on the part of voice and chatbots to resolve customer issues without human intervention.

The benefits to banks of customer service automation are obvious: AIcould lead to significant cost reductions. A recent study by Autonomous predicted that AI could lead to 1.2 million jobs being cut in the banking and lending industry, resulting in up to $450 billion in industrysavings by 2030.

Despite the potential rewards customer service automation promises, banks and other businesses need to proceed with caution in relying too heavily on voice and chatbots. The popularity of GetHuman illustrates this: It'sa website that connects consumers with human CSRs to resolve their issue. In fact, voice and chatbots often work best when augmenting rather than replacing humans. At a minimum, the option to speak to a human, if necessary, should be readily available.

Related:This Banker Explains Why Confluence of Fintech and Banks is Unavoidable

Want an example of how banks are creatively employing AI to serve customers? The Swiss bank UBS, ranked number 35 globally for its volume of assets, according to Accuity’s August 2018 global bank rankings --has partnered with Amazon to incorporate its “ Ask UBS ” service into Alexa-powered Echo speaker devices.

Ask UBS, which is aimed at UBS’s European wealth management clients, enables users to receive wide-ranging advice and analysis on global financial marketsjust by “asking” Alexa. “Ask UBS” also acts as a teaching resource, offering definitions and examples of acronyms and jargon related to the financial industry.

While Ask UBS can make a call from a UBS financial advisor to a customer’s phone upon request, it is not yet able to access individual portfolios, execute trades or perform other transactions; it can'toffer personalized advice based on a client’s holdings and goals. According to the Wall Street Journal , the reason is primarily security and privacy concerns. More in-depth and personalized service through a DVA may not be far off, though. In the article, a UBS spokesman stated that the company's aim isto make “Ask UBS” and similar tools “secure, compliant, and trustable for clients.”

Personalization

Banks have access to a wealth of customer data, including detailed demographics, website analyticsand records of online and offline transactions. By utilizing machine learning to integrate and analyze information from multiple, discrete databases to form a 360-degree customer view, banks are better positioned to personalize products, servicesand interactions based on the behavior of individual clients.

According to James Eardley , global director of industry marketing for enterprise software giant SAP, “The next step within the digital service model is for banks to price for the individual, and to negotiate that price in real time, taking personalization to the ultimate level.”

While personalized pricing of this kind may only become prevalent in the future, banks are already utilizingAI-processed behavioral data to advise individual clients on appropriate credit and savings products, based on their goals and habits. Santander, the world’s 14th largest bank , measured by its current assets, even hosted a competition , with a prize of $60,000, on the machine learning crowdsourcing site Kaggle , encouraging data scientists to write code that better “pairs products with people.”

Security

In the banking and payments industry, personalization extends far beyond marketing and product customization,into security. A growing number of banks are utilizing biometric data, like fingerprints, to replace or augment passwords and other forms of client verification.

A report by Goode Intelligence forecast that 1.9 billion bank customers will be using some form of biometric identification by 2021. The Guardian reported that U.K. bank Halifax even experimented with Bluetooth wristbands that identified a client’s unique heartbeat to authenticate account access.

In a widely discussed innovation to its popular iPhone, Apple has evolved its Face ID so that it now uses AI-powered facial-recognition techno
          Gurucul Introduces Managed Security Analytics Service      Cache   Translate Page      
Provides Dedicated Access to Data Science Experts for Design,
Management and Optimization of Behavior Based Security Systems to
Expedite Risk Detection and Response
Gurucul Introduces Managed Security Analytics Service

LOS ANGELES (BUSINESS WIRE) #EY ― Gurucul , a leader in behavior based

security and fraud analytics technology for on-premises and the cloud,

today announced Gurucul Labs, a turn-key managed security analytics

service based on the Gurucul Risk Analytics (GRA) platform which

provides the data science expertise many organizations lack to

operationalize their investments in behavior based security analytics.

Gurucul Labs combines people, processes and technology to help

organizations discover unknown threats in real-time and expedite

responses to malicious insiders, unusual usage activity, compromised

accounts or hosts, network intrusions, data exfiltration and more. The

service provides continuous machine learning algorithms and anomaly

model tuning and refinement by data scientists based on intelligence

gathered from the Carnegie Mellon US-CERT team, Gurucul’s other research

partners, and global customers.

The Gurucul Labs service provides customers the following resources:

Security Architect : to ensure a robust and scalable security
architecture (systems integration, cloud, hybrid, on-premise
deployment architecture, security architecture) and security data
validation GRA Engineer : to facilitate GRA implementation, administration
and maintenance activities Security Analyst : to support security threat research, use case
identification and design, first level triage of high-risk incidents,
case investigation, fine tuning feedback, case management and reporting Fraud Analyst: to research insider and third party fraud scenarios,
suggest data tagging and access control, investigate fraud cases,
perform impact analysis and suggest response actions Data Scientist : to review data sets, behavior models and tuning
suggestions

“Many organizations lack the in-house resources and expertise to

optimize their investments in behavior based security analytics,” said

Nilesh Dherange, chief technology officer for Gurucul. “Gurucul Labs

eliminates this roadblock, and enables customers to operationalize the

collective intelligence of Gurucul’s experts, research partners like the

Carnegie Mellon US-CERT team and best practices from the Gurucul

customer community ― to protect their environments.”

Gurucul Labs Highlights

Gurucul Labs provides an end-to-end security analytics platform

administration and maintenance service that includes:

Efficacy tracking and fine-tuning of out of the box analytical models
to find true positive incidents for real-time threat detection and
response Configuration of threat use cases to address organization specific
business and IT risks Implementation and operationalization of machine learning models
created in other systems using Gurucul STUDIO Assist organizations in deploying GRA as a centralized analytics and
risk engine to generate contextual risk prioritized alerts On-going anomaly detection, findings triage, first level
investigation, case management and reporting User and role administration, data validation, system configuration
and customization support Ongoing system maintenance and health check including resource
performance and utilization monitoring/optimization Quarterly results effectiveness reports for senior management Gurucul Labs scorecard to track anomalies, cases, model efficacy and
data ingestion trends

Availability

The Gurucul Labs managed security analytics

service is available immediately for cloud, hybrid,and on-premise

deployments.

About GRA

Gurucul Risk Analytics (GRA) is a multi-use

behavior based security and fraud analytics platform with an

architecture that supports an open choice of big data for scale, the

ability to ingest virtually any dataset for desired attributes and

includes configurable prepackaged analytics. The Gurucul GRA platform

includes UEBA, Fraud Analytics, Identity analytics and Cloud Analytics

products. In addition,

Gurucul

enables security teams to create custom machine learning

models to meet unique customer requirements without coding and minimal

data science knowledge. GRA ingests and analyzes huge volumes of data

generated when users access and interact with business applications, in

both the data center and the cloud, to generate risk scores, identify

security threats and prevent data breaches. The Gurucul GRA platform has

been successfully deployed by government agencies and Global Fortune 500

companies.

About Gurucul

Gurucul is a global cyber security and fraud

analytics company that is changing the way organizations protect their

most valuable assets, data and information from insider and external

threats both on-premises and in the cloud. Gurucul’s real-time security

analytics and fraud analytics technology combines machine learning

behavior profiling with predictive risk-scoring algorithms to predict,

prevent and detect breaches. Gurucul technology is used by Global 1000

companies and government agencies to fight cyber fraud, IP theft,

insider threat and account compromise. The company is based in Los

Angeles. To learn more, visit http://www.gurucul.com/

and follow us on LinkedIn

and Twitter .

Contacts

Marc Gendron PR

Marc Gendron, 781-237-0341

marc@mgpr.net
Gurucul Introduces Managed Security Analytics Service
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          Big Data Architect      Cache   Translate Page      
AZ-Phoenix, Big Data Architect Innovate to solve the world's most important challenges As a Big Data Architect in the Aero Advanced Analytics organization, you will be responsible for defining the platform strategy that meets the needs of our data science teams, internal processing and customer-facing web and mobile applications. This person will work closely with Corporate I/T, business analysts and peer arc
          Extreme Learning Machines (ELMs) within Artificial Intelligence (AI)      Cache   Translate Page      

Learn all about the Extreme Learning Machine (ELM) Artificial Neural Network (ANN), and how you can best leverage it within Artificial Intelligence (AI) and data science projects including within regression, alternate feature generation, and classification. We cover the ELM architecture along with the ELM packages available in Julia and Python. We’ll conclude by comparing ELMs to multilayer perceptrons (MLPs).


          The Business Aspect of Artificial Intelligence (AI) in Data Science      Cache   Translate Page      

How can our businesses best benefit from Artificial Intelligence (AI)? We cover the relevant technologies necessary to make the most out of AI, such as GPUs to enable deep learning networks, quantum computing, and the cloud. We explore which industries and applications benefit most from AI, such as finance and retail. We share the resources required for AI projects, including computing, data, and educational resources. Know the investment required to incorporate AI into data science projects so that you can best leverage this technology.


          Data Science Software Developer - Genetec - Montréal, QC      Cache   Translate Page      
Must be functional in French and English. From pioneer to leader in the security industry, Genetec has always been committed to providing the most innovative...
From Genetec - Wed, 20 Jun 2018 18:06:25 GMT - View all Montréal, QC jobs
          Data Scientist - Genetec - Montréal, QC      Cache   Translate Page      
Must be functional in French and English. We are looking to expand our data science team with a new Data Scientist!...
From Genetec - Wed, 20 Jun 2018 18:06:28 GMT - View all Montréal, QC jobs
          Data Science Software Developer - Genetec - Montréal, QC      Cache   Translate Page      
We work with the multiple infrastructure groups within Genetec to determine their various data science problems, analyze if the problems are solvable, and...
From Genetec - Wed, 20 Jun 2018 18:06:25 GMT - View all Montréal, QC jobs
          Data Scientist - Genetec - Montréal, QC      Cache   Translate Page      
We work with the multiple infrastructure groups within Genetec to determine their various data science problems, analyze if the problems are solvable, and...
From Genetec - Wed, 20 Jun 2018 18:06:28 GMT - View all Montréal, QC jobs
          Data Science Software Developer - Genetec - Montréal, QC      Cache   Translate Page      
A related study in Computer Engineering, Software Engineering or Computer Science. Once a prototype is created, we then establish a release to the team (or...
From Genetec - Wed, 20 Jun 2018 18:06:25 GMT - View all Montréal, QC jobs
          Extreme Learning Machines (ELMs) within Artificial Intelligence (AI)      Cache   Translate Page      

Learn all about the Extreme Learning Machine (ELM) Artificial Neural Network (ANN), and how you can best leverage it within Artificial Intelligence (AI) and data science projects including within regression, alternate feature generation, and classification. We cover the ELM architecture along with the ELM packages available in Julia and Python. We’ll conclude by comparing ELMs to multilayer perceptrons (MLPs).


          The Business Aspect of Artificial Intelligence (AI) in Data Science      Cache   Translate Page      

How can our businesses best benefit from Artificial Intelligence (AI)? We cover the relevant technologies necessary to make the most out of AI, such as GPUs to enable deep learning networks, quantum computing, and the cloud. We explore which industries and applications benefit most from AI, such as finance and retail. We share the resources required for AI projects, including computing, data, and educational resources. Know the investment required to incorporate AI into data science projects so that you can best leverage this technology.


          Data Science Software Developer - Genetec - Montréal, QC      Cache   Translate Page      
Designing, implementing and unit testing object-oriented, fault tolerant integrations in C# and/or F# using the latest .NET SDKs....
From Genetec - Wed, 20 Jun 2018 18:06:25 GMT - View all Montréal, QC jobs
          Data Scientist - Genetec - Montréal, QC      Cache   Translate Page      
Designing, implementing and unit testing object-oriented, fault tolerant integrations in C# and/or F# using the latest .NET SDKs....
From Genetec - Wed, 20 Jun 2018 18:06:28 GMT - View all Montréal, QC jobs
          Data Science Software Developer - Genetec - Montréal, QC      Cache   Translate Page      
Experience with Microsoft Visual Studio v. From pioneer to leader in the security industry, Genetec has always been committed to providing the most innovative...
From Genetec - Wed, 20 Jun 2018 18:06:25 GMT - View all Montréal, QC jobs
          Data Scientist - Genetec - Montréal, QC      Cache   Translate Page      
Experience with Microsoft Visual Studio v. We are looking to expand our data science team with a new Data Scientist!...
From Genetec - Wed, 20 Jun 2018 18:06:28 GMT - View all Montréal, QC jobs
          Data Science Software Developer - Genetec - Montréal, QC      Cache   Translate Page      
Software developers at Genetec use their technical aptitudes creatively in order to design and program new features, while working closely with the product...
From Genetec - Wed, 20 Jun 2018 18:06:25 GMT - View all Montréal, QC jobs
          Big Data Architect      Cache   Translate Page      
AZ-Phoenix, Big Data Architect Innovate to solve the world's most important challenges As a Big Data Architect in the Aero Advanced Analytics organization, you will be responsible for defining the platform strategy that meets the needs of our data science teams, internal processing and customer-facing web and mobile applications. This person will work closely with Corporate I/T, business analysts and peer arc
          Offer - Best Aws Training Institutes in Noida - INDIA      Cache   Translate Page      
Best Aws Training Institutes in Noida Best Aws Training Institutes in Noida,webtrackker is Amazon Thanks to the begin-united stateslike Twiggy and so on. That started the fleet of imparting on-demand cab services and meals transport services on only a few clicks. This fashion has caused the improvement of various cellular apps, providing a couple of on-demand services on the fingertips.we will study the trends wherein it changes.offered so many sparkling revolutionary ideas and creations toward automation and integration etc., and this is predicted to retain the same form of trend.International marketplace has been experiencing a terrific reaction for on-demand offerings.Though those offerings have been restrained to particular areas, it's far forecast that it'll attain to maximum locations Intelligent apps encompass technologies using non-public digital assistants which have the quality of reworking the place of business into extra interactive, with powerful conversation. Using AI technology, carrier carriers will focus on the self sufficient commercial enterprise procedures as a way to utilize the massive facts tools to the fullest, closer to superior client experience stages.So many technology are advanced within the past couple of years which have revolutionized our lives, and it is not possible to list each of them. Though era adjustments rapid with time, WEBTRACKKER TECHNOLOGY (P) LTD. C - 67, sector- 63, Noida, India. F -1 Sector 3 (Near Sector 16 metro station) Noida, India. +91 - 8802820025 0120-433-0760 EMAIL:info@webtrackker.com Website: http://webtrackker.com/amazon-web-services-aws-training-institute-in-noida.php Salesforce Training Institute in Noida Salesforce Training in noida Best Salesforce Training Institutes in Noida Best Aws Training Institutes in Noida best aws training in noida aws training institute in noida best data science training institute in delhi python Training Institute in noida sas Training Institute in noida linux Training Institute in noida
          Crescita digitale, in Italia servono data scientist ed esperti in cybersecurity      Cache   Translate Page      

In Italia c’è sempre più bisogno di data scientist e di esperti di cyber security e cloud computing, con una variazione tra il 2014 e il 2017, rispettivamente del 369%, 388% e 280%. E in Puglia il quadro è molto simile a quello nazionale, se è vero che le imprese IT attive sul territorio regionale […]

The post Crescita digitale, in Italia servono data scientist ed esperti in cybersecurity appeared first on Key4biz.


          Data Science Software Developer - Genetec - Montréal, QC      Cache   Translate Page      
Today, we offer world-class IP security solutions with our unified security platform encompassing license plate recognition (LPR), video surveillance and access...
From Genetec - Wed, 20 Jun 2018 18:06:25 GMT - View all Montréal, QC jobs
          Data Scientist - Genetec - Montréal, QC      Cache   Translate Page      
Whether it be our gym, our health conferences, our games lounge (PS3, foosball table, pool table, Ping-Pong table), our on-site Bistro offering delectable,...
From Genetec - Wed, 20 Jun 2018 18:06:28 GMT - View all Montréal, QC jobs
          WEB DEVELOPER (DIGITAL AND DATA SCIENCE) - The Globe and Mail - Toronto, ON      Cache   Translate Page      
Develop a new, real-time, advanced analytics dashboard for editorial intelligence. This role is a technical position on a diverse team building a next...
From The Globe and Mail - Mon, 16 Jul 2018 23:48:51 GMT - View all Toronto, ON jobs
          Hot Topics of 2017: Behavior, Direct Electrical Stimulation, Computational Psychiatry, and more      Cache   Translate Page      

Hot topic is the way that we rhyme
Hot topic is the way that we rhyme
. . .
Carol Rama and Elanor Antin
Yoko Ono and Carolee Schneeman
You're getting old, that's what they'll say, but
Don't give a damn I'm listening anyway

Le Tigre, Hot Topic


What were some of the notable neuroscience topics and advances of 2017?
Here is a short and idiosyncratic list:


1. The Return of Behavior

Krakauer JW, Ghazanfar AA, Gomez-Marin A, MacIver MA, Poeppel D. Neuroscience Needs Behavior: Correcting a Reductionist Bias. Neuron. 2017 Feb 8;93(3):480-490.

see The Big Ideas in Cognitive Neuroscience, Explained


2. Direct Electrical Stimulation of the Human Brain (DARPA style)  but continuous DBS for nothing psychiatric yet.

Ezzyat Y, Kragel JE, Burke JF, Levy DF, Lyalenko A, Wanda P, O'Sullivan L, Hurley KB, Busygin S, Pedisich I, Sperling MR, Worrell GA, Kucewicz MT, Davis KA, Lucas TH, Inman CS, Lega BC, Jobst BC, Sheth SA, Zaghloul K, Jutras MJ, Stein JM, Das SR, Gorniak R, Rizzuto DS, Kahana MJ. (2017). Direct Brain Stimulation Modulates Encoding States and Memory Performance in Humans. Curr Biol. 27(9):1251-1258.

Wu H, Miller KJ, Blumenfeld Z, Williams NR, Ravikumar VK, Lee KE, Kakusa B, Sacchet MD, Wintermark M, Christoffel DJ, Rutt BK, Bronte-Stewart H, Knutson B, Malenka RC, Halpern CH. (2017). Closing the loop on impulsivity via nucleus accumbens delta-band activity in mice and man. Proc Natl Acad Sci Dec 18. [Epub ahead of print].

Inman CS, Manns JR, Bijanki KR, Bass DI, Hamann S, Drane DL, Fasano RE, Kovach CK, Gross RE, Willie JT. (2017). Direct electrical stimulation of the amygdala enhances declarative memory in humans. Proc Natl Acad Sci Dec 18. [Epub ahead of print].

see Amygdala Stimulation in the Absence of Emotional Experience Enhances Memory for Neutral Objects


3. Computational Psychiatry (in theory, not in reality)...  But how about:

Powers AR, Mathys C, Corlett PR. (2017). Pavlovian conditioning-induced hallucinations result from overweighting of perceptual priors. Science. 357(6351):596-600.


4. Debates About Prediction vs. Explanation

Yarkoni T, Westfall J. (2017). Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning. Perspect Psychol Sci. 12(6):1100-1122.



So many roads and so much opinion
So much shit to give in, give in to
So many rules and so much opinion
So much bullshit but we won't give in
Stop, we won't stop
Don't you stop
I can't live if you stop 

ibid



Hey neurokids! Forget about biology and psychology. Get your degree in engineering, statistics, mathematics, machine learning, or data science!! Or else you'll end up useless like the lost generation of neuroscience Ph.D.'s......


5. Opto-anything



Tammy Rae Carland and Sleater-Kinney
Vivienne Dick and Lorraine O'Grady
Gayatri Spivak and Angela Davis
Laurie Weeks and Dorothy Allison
Stop, don't you stop
Please don't stop
We won't stop

ibid



6. Pretty much anything by @KordingLab and by @gallantlab is revered.


7. Then there's all that Bayesian Brain Markov Blanket Free Energy Principle stuff, but @neuroconscience is way more qualified to tout this work.


8. Manifolds.


Gertrude Stein, Marlon Riggs, Billie Jean King, Ut, DJ Cuttin Candy,
David Wojnarowicz, Melissa York, Nina Simone, Ann Peebles, Tammy Hart,
The Slits, Hanin Elias, Hazel Dickens, Cathy Sissler, Shirley Muldowney,
Urvashi vaid, Valie Export, Cathy Opie, James Baldwin,
Diane Dimassa, Aretha Franklin, Joan Jett, Mia X, Krystal Wakem,
Kara Walker, Justin Bond, Bridget Irish, Juliana Lueking,
Cecelia Dougherty, Ariel Skrag, The Need, Vaginal Creme Davis,
Alice Gerard, Billy Tipton, Julie Doucet, Yayoi Kusama, Eileen Myles
Oh no no no don't stop stop............ 

ibid


{I don't know about you, but I'm a little burned out on functional connectivity and the human connectome.}





Kathleen Hanna on Becoming a Brand

          Data Scientist - TECHNICA CORPORATION - Dulles, VA      Cache   Translate Page      
Technica Corporation is seeking a Senior Data Scientist. To support our internal Innovation, Research and....
From Technica Corporation - Sat, 08 Sep 2018 10:29:14 GMT - View all Dulles, VA jobs
          Data Scientist - NetMotion Software - Victoria, BC      Cache   Translate Page      
There is a strong, collaborative relationship between managers, architects, developers, program managers, SDETs and QA engineers as partners in the delivery of...
From NetMotion Software - Thu, 13 Sep 2018 08:01:32 GMT - View all Victoria, BC jobs
          Discourse.ai Unveils the First AI-based System of Record for Customer Conversations, Unlocking the Value of Customer Conversation Data      Cache   Translate Page      
...System of Record for customer conversation data to identify conversation patterns and train agent assistants and chatbots. As a result, business analysts, data scientists, and bot developers are forced into a manual trial and error process that doesn't fully leverage ...

          Data Scientist - Canadian National Railway - Montréal, QC      Cache   Translate Page      
IVADO, Vector Institute, Scale.AI) to design and implement applied AI/data science models that solve real world problems. Why do you want this job?...
From Canadian National Railway - Tue, 11 Sep 2018 07:15:16 GMT - View all Montréal, QC jobs
          Sr. Data Scientist - Shutterstock - Montréal, QC      Cache   Translate Page      
Role: Shutterstock is looking for an experienced data scientist to join our world-class artificial intelligence team! The Artificial Intelligence team's...
From Shutterstock - Mon, 10 Sep 2018 16:21:19 GMT - View all Montréal, QC jobs
          Senior Data Scientist - Prudential - Newark, NJ      Cache   Translate Page      
Identify analytical solutions for business problems. And a passion for generating business impact. Develop and maintain consultative relationships with key...
From Prudential - Wed, 11 Jul 2018 21:51:46 GMT - View all Newark, NJ jobs
          Data Scientist - Prudential - Newark, NJ      Cache   Translate Page      
And a passion for generating business impact. Develop and maintain consultative relationships with key business stakeholders....
From Prudential - Wed, 11 Jul 2018 21:49:38 GMT - View all Newark, NJ jobs
          Principal Market Validation Specialist - PTC - Needham, MA      Cache   Translate Page      
Advance knowledge and experience with Machine Learning / Data Science / Analytics. Customer Satisfaction focus, both internal and external, with strong...
From PTC - Wed, 16 May 2018 14:29:21 GMT - View all Needham, MA jobs
          Senior Data Scientist - Mogo Finance Technology Inc. - Toronto, ON      Cache   Translate Page      
Built mobile first, users can sign up for a free MogoAccount in only three minutes and get access to 6 products including free credit score monitoring, identity...
From Mogo Finance Technology Inc. - Wed, 22 Aug 2018 22:43:29 GMT - View all Toronto, ON jobs
          Data Scientist - Royal Sporting House - UAE      Cache   Translate Page      
Maintain a good technical knowledge of analytical and modelling software. No two days are the same at Al-Futtaim, no matter what role you have....
From Royal Sporting House - Sun, 19 Aug 2018 14:00:39 GMT - View all UAE jobs
          Lead Educator, Data Science - BrainStation - Vancouver, BC      Cache   Translate Page      
Have you been to a campus or joined an online learning opportunity? We are actively seeking individuals that believe in lifelong learning and that have taken...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Vancouver, BC jobs
          Associate Educator, Data Science - BrainStation - Vancouver, BC      Cache   Translate Page      
Have you been to a campus or joined an online learning opportunity? We are actively seeking individuals that believe in lifelong learning and that have taken...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Vancouver, BC jobs
          Educator, Data Science - BrainStation - Toronto, ON      Cache   Translate Page      
Have you been to a campus or joined an online learning opportunity? We are actively seeking individuals that believe in lifelong learning and that have taken...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Toronto, ON jobs
          Associate Educator, Data Science - BrainStation - Toronto, ON      Cache   Translate Page      
Have you been to a campus or joined an online learning opportunity? We are actively seeking individuals that believe in lifelong learning and that have taken...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Toronto, ON jobs
          Data Scientist - Canadian National Railway - Montréal, QC      Cache   Translate Page      
IVADO, Vector Institute, Scale.AI) to design and implement applied AI/data science models that solve real world problems. Why do you want this job?...
From Canadian National Railway - Tue, 11 Sep 2018 07:15:16 GMT - View all Montréal, QC jobs
          Sr. Data Scientist - Shutterstock - Montréal, QC      Cache   Translate Page      
Role: Shutterstock is looking for an experienced data scientist to join our world-class artificial intelligence team! The Artificial Intelligence team's...
From Shutterstock - Mon, 10 Sep 2018 16:21:19 GMT - View all Montréal, QC jobs
          Scientist, Data Science (1 of 2) - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Consideration will be given to Raritan, NJ; Janssen Research &amp; Development LLC, a Johnson &amp; Johnson company, is recruiting for a Scientist, Data Science....
From Johnson & Johnson Family of Companies - Wed, 05 Sep 2018 16:05:17 GMT - View all Spring House, PA jobs
          Senior Scientist, Data Science (1 of 2) - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Consideration will be given to Raritan, NJ; Janssen Research &amp; Development LLC, a Johnson &amp; Johnson company, is recruiting for a Senior Scientist, Data Science....
From Johnson & Johnson Family of Companies - Wed, 05 Sep 2018 16:05:17 GMT - View all Spring House, PA jobs
          Senior Analyst, Data Science (1 of 2) - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Consideration will be given to Raritan, NJ; Janssen Research &amp; Development LLC, a Johnson &amp; Johnson company, is recruiting for a Senior Analyst, Data Science....
From Johnson & Johnson Family of Companies - Wed, 05 Sep 2018 04:05:45 GMT - View all Spring House, PA jobs
          Data Scientist      Cache   Translate Page      
NY-Jericho, Publishers Clearing House’s Analytical Science team is looking for a strong Data Scientist who is passionate about using the power of algorithms to drive customer engagement and monetization. As a part of our team, you will be utilizing enormous volumes of data on customers, PCH products, channels, and developing advanced statistical algorithms to solve unique business problems. This is an excitin
          Data Scientist - Proyectos I+D - ECB Engineering Firm - Madrid, España      Cache   Translate Page      
Si te gusta enfrentarte cada día a nuevos retos, ¡eres la persona que necesitamos! Buscamos un perfil Data Scientist con un mínimo de entre 2-3 años de experiencia profesional en proyectos de NLP para unirse al área de innovación en uno de nuestros principales clientes. Tendrás la oportunidad de colaborar en proyectos de tal ámbito, velando por el avance de la investigación en pruebas de concepto y pilotos asociados a cualquier área estratégica de la compañía. Podrás colaborar en...
          Data Scientist - Canadian National Railway - Montréal, QC      Cache   Translate Page      
IVADO, Vector Institute, Scale.AI) to design and implement applied AI/data science models that solve real world problems. Why do you want this job?...
From Canadian National Railway - Tue, 11 Sep 2018 07:15:16 GMT - View all Montréal, QC jobs
          Sr. Data Scientist - Shutterstock - Montréal, QC      Cache   Translate Page      
Role: Shutterstock is looking for an experienced data scientist to join our world-class artificial intelligence team! The Artificial Intelligence team's...
From Shutterstock - Mon, 10 Sep 2018 16:21:19 GMT - View all Montréal, QC jobs
          Business Analyst - Hookit - San Diego, CA      Cache   Translate Page      
Collaborate with data science, operations leads, and product managers to understand how insights can be scaled to more customers....
From Hookit - Thu, 13 Sep 2018 02:38:03 GMT - View all San Diego, CA jobs
          Data Scientist - TECHNICA CORPORATION - Dulles, VA      Cache   Translate Page      
Technica Corporation is seeking a Senior Data Scientist. To support our internal Innovation, Research and....
From Technica Corporation - Sat, 08 Sep 2018 10:29:14 GMT - View all Dulles, VA jobs
          Software Development Principal Engineer – Data Scientist - DELL - Austin, TX      Cache   Translate Page      
Learn more about Diversity and Inclusion at Dell here. Selecting features, building and optimizing classifiers using machine learning techniques....
From Dell - Sat, 07 Jul 2018 11:22:08 GMT - View all Austin, TX jobs
          Sales Engineer - Hitachi Vantara - New York, NY      Cache   Translate Page      
Account Managers, internal specialists and customers. Understanding of Data Science and Machine Learning....
From Hitachi Vantara - Sat, 04 Aug 2018 04:47:47 GMT - View all New York, NY jobs
          University receives 2.5 million euros for research in data science      Cache   Translate Page      

Answer market needs

Entitled “ERA Chair in Mathematical Statistics and Data Science for the University of Luxembourg – SanDAL”, this action aims to fill a gap in current research activities to position Luxembourg as a key player in mathematics research. Prof. Jean-Marc Schlenker, Head of the Mathematics Research Unit (RMATH) at the University of Luxembourg explains: “Within a few years only, we have succeeded in building an internationally renowned department in the fields of probability theory, geometry, number theory and mathematical physics. However, with the growing importance of data science in all sectors of the economy and especially in logistics, healthcare, ICT, manufacturing and finance, Luxembourg suffers from a lack of skilled scientists, engineers and researchers. Therefore, this outstanding award will enable us to meet the job market’s needs.”

Develop research and training

The project will boost research and training in mathematical statistics and data science. The ERA Chair research activities will focus on two areas: “High-Dimensional Data Analysis” to have a deeper understanding of emerging computational tools and “New Mathematical Tools for Contemporary Statistics” to develop new and more sophisticated statistical methods. In addition, new teaching programmes at Master and PhD levels will be elaborated to provide future students with the necessary skills in mathematical statistics and data science to understand, develop and apply new tools in various fields. Prof. Stéphane Pallage, Rector of the University of Luxembourg, sees all the benefits of this new funding: “The multidisciplinary ERA Chair will increase the international reputation of the University of Luxembourg by attracting and retaining experienced researchers. Moreover, it will support the competitiveness of the Luxembourg economy by transferring knowledge and know-how”.

Recruitment of an expert team

To ensure the development of a strong and sustainable new direction of research, the University of Luxembourg will recruit highly qualified research staff, namely a permanent professor who will act as the ERA Chair Holder, two postdoctoral researchers and three PhD candidates. Applications for the position of Professor in Mathematical Statistics and Data Science are open until 1 January 2019 via the job portal of the University of Luxembourg.

Figure: overall concept of the SanDAL project

Figure: overall concept of the SanDAL project: research and innovation policy context, Action Plan, ERA Chair and expected impacts

The ERA Chair SanDAL has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 811017.

---

 


          You Ask, I Answer: The Promise of AI and Data for Marketing      Cache   Translate Page      

Denis asks, “What is the big promise that AI holds when it comes to data? What types of solutions do you see emerging from this that will help marketers?” Look at the data science lifecycle. Every repeatable choice along this lifecycle has at least some portion which is a repetitive, predictable process. Where we’ll see […]

The post You Ask, I Answer: The Promise of AI and Data for Marketing appeared first on Christopher S. Penn Marketing Blog.


          Lead Educator, Data Science - BrainStation - Vancouver, BC      Cache   Translate Page      
Strong command of querying and programming languages (Python, SQL, R), and visualization tools (Tableau, Python packages), as well as experience applying...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Vancouver, BC jobs
          Associate Educator, Data Science - BrainStation - Vancouver, BC      Cache   Translate Page      
Strong command of querying and programming languages (Python, SQL, R), and visualization tools (Tableau, Python packages), as well as experience applying...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Vancouver, BC jobs
          Educator, Data Science - BrainStation - Toronto, ON      Cache   Translate Page      
Strong command of querying and programming languages (SQL, Python, R), and visualization tools (Tableau, Python packages, etc.), as well as experience applying...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Toronto, ON jobs
          Associate Educator, Data Science - BrainStation - Toronto, ON      Cache   Translate Page      
Strong command of querying and programming languages (SQL, Python, R) and visualization tools (Tableau, Python packages, etc.), as well as experience applying...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Toronto, ON jobs
          Data Scientist Job - SAIC - Reston, VA      Cache   Translate Page      
For information on the benefits SAIC offers, see My SAIC Benefits. SAIC is hiring two Data Scientists in Reston, VA....
From SAIC - Sat, 08 Sep 2018 02:46:43 GMT - View all Reston, VA jobs
          Data Scientist - FedEx Services - Brookfield, WI      Cache   Translate Page      
Women’s Business Enterprise National Council “America’s Top Corporations for Women’s Business Enterprises” - 2016....
From FedEx - Wed, 29 Aug 2018 00:08:19 GMT - View all Brookfield, WI jobs
          Principal Technologist - Machine Learning and Data Science - Blue Origin - Kent, WA      Cache   Translate Page      
While in this role, you will leverage your extensive experience in machine learning and data science to accelerate and innovate across business areas to drive...
From Blue Origin - Wed, 13 Jun 2018 05:31:45 GMT - View all Kent, WA jobs
          IBM Talks Data Platforms for AI      Cache   Translate Page      
An IBM blog post.   Agree with the issues, is this the solution?  Its about the data and the process being improved.   Choosing a Platform is a big deal.   By IBM:

Winning with AI
Since the year 2000, 52% of the companies that make up the Fortune 500 have disappeared. They have been acquired, succumbed to performance atrophy, or declared bankruptcy. In this hyper-competitive marketplace, winners and losers are being declared every day. And while artificial intelligence (AI) can be the valve to these pressures, for many, drafting a playbook for actually winning with AI remains daunting. 

Also, consider a recent IDC Cloud and AI Adoption Survey[1] in which more than 80% of respondents said they plan to move, or repatriate, data and workloads from public cloud environments to private clouds or on-premises locations over the next year, as the initial expectations of a single public cloud provider were not realized. These dynamics add to the confusion that every CEO, CIO, CTO, and CDO faces on a daily basis.

So, what precisely is dragging down projects and preventing companies from delivering measurable business value? I see three recurring patterns:

Companies have been accumulating data at an amazing pace for years, but are still challenged with how to store, manage, and control access. They need a new, modern approach;

The pressure to innovate is mounting. Companies create a chief data office or a data science center of excellence, but do not always have the right model for organizational success;

Small successes only scale when models are put into production and companies adapt their business processes, but unfortunately, this doesn’t occur very often. Scale requires platform thinking and technology.  Companies are at a critical juncture. They must be able to find and scale insights on demand if they want to climb the Ladder to AI.

Enter the Data Platform

This summer IBM launched an innovative approach and solution to this conundrum. Our new IBM Cloud Private for Data (ICP for Data) is a modern data platform designed to integrate data science, data engineering and application building into an environment that companies can use to uncover previously hidden insights from their data. Built on IBM Cloud Private, ICP for Data includes an enterprise meta-data catalog as the centerpiece along with services for data federation/virtualization, data warehousing, data integration, data science / machine learning and embedded dash-boarding.


Rob Thomas, General Manager, IBM Analytics.

It is designed to connect all data across an enterprise seamlessly, starting with enterprise data, and offers all its capabilities as data micro services. Consider it the highway system for the data revolution.  .... " 
          Data Scientist Job - SAIC - Reston, VA      Cache   Translate Page      
Work will be performed primarily with internal company contacts. Determines the appropriate analytics based on the data and the desired outcomes, using...
From SAIC - Sat, 08 Sep 2018 02:46:43 GMT - View all Reston, VA jobs
          Data Scientist Associate - Montreal AI Accelerator, powered by Techstars - Techstars - Montréal, QC      Cache   Translate Page      
Techstars Startup Programs, Techstars Mentorship-Driven Accelerator Programs, Techstars Corporate Innovation Partnerships, and the Techstars Venture Capital...
From Techstars - Wed, 11 Jul 2018 17:49:02 GMT - View all Montréal, QC jobs
          Senior Data Scientist - Prudential - Newark, NJ      Cache   Translate Page      
Identify analytical solutions for business problems. And a passion for generating business impact. Develop and maintain consultative relationships with key...
From Prudential - Wed, 11 Jul 2018 21:51:46 GMT - View all Newark, NJ jobs
          Data Scientist - Prudential - Newark, NJ      Cache   Translate Page      
And a passion for generating business impact. Develop and maintain consultative relationships with key business stakeholders....
From Prudential - Wed, 11 Jul 2018 21:49:38 GMT - View all Newark, NJ jobs
          DATA ENGINEER (DIGITAL AND DATA SCIENCE) - The Globe and Mail - Toronto, ON      Cache   Translate Page      
This role is a technical position on a diverse team building a next generation editorial intelligence data platform. DATA ENGINEER (DIGITAL AND DATA SCIENCE)....
From The Globe and Mail - Tue, 28 Aug 2018 03:17:59 GMT - View all Toronto, ON jobs
          Data Scientist - Game Hive - Toronto, ON      Cache   Translate Page      
Our most popular games include the Tap Titans, Beat the Boss and Antrim Escape series (and many, more!). Game Hive is building a new generation of casual mobile...
From Indeed - Wed, 15 Aug 2018 13:34:49 GMT - View all Toronto, ON jobs
          Facebook pages and groups scraping for email addresses - Upwork      Cache   Translate Page      
We want to get a full email list of Facebook group members of certain groups. The name of the fb groups to scrape will be provided once the job is accepted.

Posted On: September 13, 2018 16:40 UTC
ID: 214194807
Category: Data Science & Analytics > Data Mining & Management
Skills: Data Mining, Data Scraping, Web Scraping
Country: United Kingdom
click to apply
          Senior Operations Data Scientist - Western Digital - Milpitas, CA      Cache   Translate Page      
951 SanDisk Drive, Milpitas CA 95035. Conduct quantitative modeling to provide solutions to new business problems....
From Indeed - Wed, 12 Sep 2018 22:30:34 GMT - View all Milpitas, CA jobs
          Lead Educator, Data Science - BrainStation - Vancouver, BC      Cache   Translate Page      
Strong command of querying and programming languages (Python, SQL, R), and visualization tools (Tableau, Python packages), as well as experience applying...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Vancouver, BC jobs
          Associate Educator, Data Science - BrainStation - Vancouver, BC      Cache   Translate Page      
Strong command of querying and programming languages (Python, SQL, R), and visualization tools (Tableau, Python packages), as well as experience applying...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Vancouver, BC jobs
          Educator, Data Science - BrainStation - Toronto, ON      Cache   Translate Page      
Strong command of querying and programming languages (SQL, Python, R), and visualization tools (Tableau, Python packages, etc.), as well as experience applying...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Toronto, ON jobs
          Associate Educator, Data Science - BrainStation - Toronto, ON      Cache   Translate Page      
Strong command of querying and programming languages (SQL, Python, R) and visualization tools (Tableau, Python packages, etc.), as well as experience applying...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Toronto, ON jobs
          WORSHIP WORKS: Research Analyst, Healthcare (Christian Ministry)      Cache   Translate Page      
Excellent, subject to experience: WORSHIP WORKS: Develop your DATA SCIENCE skills in the global healthcare industry, while putting your faith into every aspect of your career as a WORKPLACE MINISTER. Maidstone, Kent, UK
          SAP’s cloud analytics update offers insights in seconds, not months      Cache   Translate Page      

With a refresh of its cloud analytics tools, SAP hopes to bring users new insights into their data more quickly and take some of the workload off the IT department.

It’s not that the previous analytics tools were all that slow; it’s just that to get the most out of some of their features, you needed a team of data scientists to build the right reports.

Now the company is using machine learning to help SAP Analytics Cloud users zoom in on key data correlations, according to Mike Flannagan, SAP’s senior vice president of analytics.

To read this article in full, please click here


          Senior Data Scientist - Prudential - Newark, NJ      Cache   Translate Page      
Identify analytical solutions for business problems. And a passion for generating business impact. Develop and maintain consultative relationships with key...
From Prudential - Wed, 11 Jul 2018 21:51:46 GMT - View all Newark, NJ jobs
          Data Scientist - Prudential - Newark, NJ      Cache   Translate Page      
And a passion for generating business impact. Develop and maintain consultative relationships with key business stakeholders....
From Prudential - Wed, 11 Jul 2018 21:49:38 GMT - View all Newark, NJ jobs
          Data Scientist - Canadian National Railway - Montréal, QC      Cache   Translate Page      
IVADO, Vector Institute, Scale.AI) to design and implement applied AI/data science models that solve real world problems. Why do you want this job?...
From Canadian National Railway - Tue, 11 Sep 2018 07:15:16 GMT - View all Montréal, QC jobs
          Data Scientist - Yamaha - Cypress, CA      Cache   Translate Page      
Develop statistical models, machine learning-based tools or processes to measure and manage business performance....
From Yamaha - Wed, 22 Aug 2018 00:54:18 GMT - View all Cypress, CA jobs
          Vincent Granville posted a blog post      Cache   Translate Page      
Vincent Granville posted a blog post

Analytics Translator – The Most Important New Role in Analytics

Summary:  The role of Analytics Translator was recently identified by McKinsey as the most important new role in analytics, and a key factor in the failure of analytic programs when the role is absent. The role of Analytics Translator was recently identified by McKinsey as the most important new role in analytics, and a key factor in the failure of analytic programs when the role is absent.As our profession of data science has evolved, any number of authors including myself has offered different taxonomies to describe the differences among the different ‘tribes’ of data scientists.  We may disagree on the categories but we agree that we’re not all alike.Ten years ago, around the time that Hadoop and Big Data went open source there was still a perception that data scientists should be capable of performing every task in the analytics lifecycle. The obvious skills were model creation and deployment, and data blending and munging.  Other important skills in this bucket would have included setting up data infrastructure (data lakes, streaming architectures, Big Data NoSQL DBs, etc.).  And finally the skills that were just assumed to come with seniority, storytelling (explaining it to executive sponsors), and great project management skills.Frankly, when I entered the profession, this was true and for the most part, in those early projects, I did indeed do it all.Data Science – A Profession of SpecialtiesIt’s fair to say that today nobody expects this.  Ours is rapidly becoming a field of specialists, defined by data types (NLP, image, streaming, classic static data), role (data engineer, junior data scientist, senior data scientist), or by use cases (predictive maintenance, inventory forecasting, personalized marketing, fraud detection, chatbot UIs, etc.).  These aren’t rigid boundaries and a good data scientist may bridge several of these, but not all.Read full article here. (By Bill Vorhies)See More

          Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Knowledge and experience on applying statistical and machine learning techniques on real business data....
From Lincoln Financial Group - Mon, 27 Aug 2018 18:47:26 GMT - View all Boston, MA jobs
          Sr. Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Implements and maintains predictive and statistical models to identify business opportunities and solve complex business problems....
From Lincoln Financial Group - Fri, 17 Aug 2018 18:34:07 GMT - View all Boston, MA jobs
           Comment on Question of the Week: September 11th, 2018. by Owen Michael       Cache   Translate Page      
In Britain you have to choose your degree subject before even applying to University, and to change (except in the first couple of weeks in a very narrow band) you basically have to apply all over again, and there are generally very few modules you do outside your course. I was always planning to do a Maths-focused degree once I'd started applying (although when I picked my A-level options for Year 12 and 13 I meant to apply for a Politics, Philosophy, and Economics one and I'm still not quite sure when I changed my mind), but in hindsight I feel I made a mistake doing a straight Maths degree instead of taking the Maths, Operational Research, Statistics and Economics one I also got onto). I was planning to do a master's in Data Science this year - that got scuppered by doing (very narrowly) too badly to get into any I could find, but I'm hoping I might be able to with some related work experience in the future.
          Data science hub to replace Illini Hall      Cache   Translate Page      
Illini Hall will be demolished and replaced with a new facility, which will house a data science hub and classrooms for the statistics and mathematics ...
          Army uses unconventional challenge process to advance data science for electronic warfare      Cache   Translate Page      
Out of 150 teams, it picked Aerospace Corporation, a federally-funded research and development center; an Australian data science team called ...
          Why Machine Learning Lifecycle Management Is Important      Cache   Translate Page      
Data science and machine learning are becoming the critical building blocks for data-driven organisations. Now accelerating ML lifecycle ...
          What is the Role of an AI Software Engineer in a Data Science Team?      Cache   Translate Page      
This has resulted in the formation of a new position in a typical data science team-artificial intelligence (AI) software engineer. A potential justification ...
          SAP’s cloud analytics update offers insights in seconds, not months      Cache   Translate Page      
It's not that the previous analytics tools were all that slow; it's just that to get the most out of some of their features, you needed a team of data scientists ...
          Lautenbach: LexisNexis champions local student      Cache   Translate Page      
It was my pleasure earlier this week to honor LexisNexis Risk Solutions, which partners with New College of Florida's Data Science Program.
          How Did Artificial Intelligence Proof Of Concepts Turn Into Such Huge Roadblocks?      Cache   Translate Page      
As part of this process, data scientists determine under what parameters their models converge most efficiently given the available training data, while ...
          Data Science Manager      Cache   Translate Page      
As a manager on the data science team, you will own a critical piece of the Firefox organization. Data scientists work closely with product and ...
          Postdoctoral Research Fellow/Research Fellow (Data Science and Analytics) - Australia      Cache   Translate Page      
It is an exciting time to get involved with the School of Information Technology and Electrical Engineering, located on UQ’s St. Lucia campus.  The School is ramping up its investment in teaching,...
          Data Scientist Senior Lead UP to 150k PA TAX FREE!      Cache   Translate Page      
Data Scientist Senior Lead UP to 150k PA TAX FREE! Our customer is a rapidly growing and leading data science firm based in Riyadh with proven track record of excellence in supporting and growing the analytics ecosystem in Saudi Arabia They are a trusted analytics partner for the largest government organizations in Saudi Arabia as well as ma
          Principal Data Scientist - DMA Global - Montréal, QC      Cache   Translate Page      
You will ideally have a Master's or PhD in Statistics, Mathematics, Computer Science, Electrical Engineering or another quantitative field, combined with...
From Indeed - Thu, 13 Sep 2018 17:03:53 GMT - View all Montréal, QC jobs
          Terry Chay: Which has better packages, Python or PHP?      Cache   Translate Page      

Terry Chay has an interesting post on his site that wonders which language has better packages - PHP or Python?

It depends on the target utility. In the Python world, the most common package installer is pip; the PHP world didn’t settle on a dominant format/installation for packages until composer, and that was relatively recently (last 4 years).

[...] So which has better packages? The answer is it depends on the domain. In nearly any language you can find an adequate package for any of your needs, but overall you will find the packages are higher quality, more up-to-date, and sometimes just better overall in the domain the language seems to target well.

He starts off by talking some about PHP and Python's origins - PHP as a web-focused language and Python as more general purpose - and how this influenced their package implementations. He then shares his opinions on which kind of packages are a more natural fit for which languages:

  • for data science/AI/ML applications, Python
  • for DevOps, relying on other tools (Puppet/Chef/Ansible/etc) is better
  • For server-side web-based packages, I feel PHP and Composer [are the solution]

He also includes some thoughts about other languages - Ruby, Javascript, Go - and their own package managers.


          Data Engineer      Cache   Translate Page      
CA-Los Angeles, job summary: * Design and develop new data processing systems using various ETL tools and languages like Python/SSIS/ADF and open source technologies to enable data scientists to consume and understand data faster and easier. * Work with Business Intelligence and Data Scientists to understand data needs and ingest rich data sources such as external claims data feeds, Electronic Health Record data,
          Emploi IT : l’État recrute des data scientists et des développeurs      Cache   Translate Page      
L'appel à candidatures est lancé. 32 data scientists, développeurs et designers formeront la troisième promotion d'entrepreneurs d’intérêt général portée par la mission Etalab.
          2019 Internship - Bellevue, WA- Data Science - Expedia - Bellevue, WA      Cache   Translate Page      
June 17 – September 6. As a Data Scientist Intern within Expedia Group, you will work with a dynamic teams of product managers and engineers across multiple...
From Expedia - Fri, 31 Aug 2018 21:36:06 GMT - View all Bellevue, WA jobs
          R Deep Learning Essentials      Cache   Translate Page      

Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for computer vision and text Apply deep learning techniques in cloud for large-scale processing Build, train, and optimize neural network models on a range of datasets Book Description Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You'll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects. What you will learn Build shallow neural network prediction models Prevent models from overfitting the data to improve generalizability Explore techniques for finding the best hyperparameters for deep learning models Create NLP models using Keras and TensorFlow in R Use deep learning for computer vision tasks Implement deep learning tasks, such as NLP, recommendation systems, and autoencoders Who this book is for This second edition of R Deep Learning Essentials is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. Fundamental understanding of the R language is necessary to get the most out of this book. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.


          Data Engineer      Cache   Translate Page      
CA-Los Angeles, job summary: * Design and develop new data processing systems using various ETL tools and languages like Python/SSIS/ADF and open source technologies to enable data scientists to consume and understand data faster and easier. * Work with Business Intelligence and Data Scientists to understand data needs and ingest rich data sources such as external claims data feeds, Electronic Health Record data,
          Asean Foundation & SAP Gelar Kompetisi ADSE, Ini Pemenangnya      Cache   Translate Page      
Asean Foundation dan SAP mengadakan kompetisi Asean Data Science Explorer (ADSE) yang dimenangi Tim OWL dari Universinas Bina Nusantara (Binus) dan berhasil menjadi juara pada tingkat nasional.
          Gurucul introduces managed security analytics service      Cache   Translate Page      

Gurucul announced Gurucul Labs, a managed security analytics service based on the Gurucul Risk Analytics (GRA) platform which provides the data science expertise many organizations lack to operationalize their investments in behavior based security analytics. Gurucul Labs combines people, processes and technology to help organizations discover threats in real-time and expedite responses to malicious insiders, unusual usage activity, compromised accounts or hosts, network intrusions, data exfiltration and more. The service provides machine learning algorithms and … More

The post Gurucul introduces managed security analytics service appeared first on Help Net Security.


          Product Manager I      Cache   Translate Page      
IL-Chicago, Job DescriptionJob #: 885476 Job Description: As a Product Manager, you will lead nimble cross-functional teams of data scientists and business analysts who share your passion and drive for solving human and business problems with data. In this role, you will: • Seek to understand customers financial needs and deliver new technology and experiences that improve our customers ability to manage thei
          SourceMedia Makes Two Additions to Its C-Suite | People on the Move      Cache   Translate Page      

People

[caption id="attachment_135385" align="alignright" width="150"] Jeff Mancini[/caption]

SourceMedia appointed two new c-level executives this week. Former chief marketing officer for the real estate development company Related Companies, Jeff Mancini, is taking on the role of chief strategy officer, and Christian Ward is joining SourceMedia as chief data officer from Yext, where he most recently served as the EVP of global data partnerships. In their new roles, each will be tasked with executing key aspects of SourceMedia’s growth plan including the coordination of new products, business models and brands.

Mancini has over 20 years of experience as a marketing leader with a resume that includes developing R/GA’s global innovation practice and helping to transform Interbrand into a digital thought leader. Ward also has over two decades of experience driving data innovation across financial service, digital marketing and media companies, including serving chief data officer at Infogroup and as global head of content innovation at Thomson Reuters.

[caption id="attachment_135387" align="alignright" width="150"] Christian Ward[/caption]

“We are reimagining what the journey for our members will look like next year and into the future. This will require a new strategy to connect all of the exciting forms of content and live experiences we currently offer, and extend that into new categories,” said Gemma Postlethwaite, CEO of SourceMedia, in a statement.

Mancini and Ward are former colleagues of Postlethwaite's, at S&P Global-owned PIRA Energy Group and Thomson Reuters, respectively. “I look forward to working with Jeff, Christian and the rest of our leadership team on bringing to life this strategy and the next phase of the SourceMedia business transformation,” she said.

These hires come days after CMO Matt Yorke moved to Northstar Travel Group to take on the role of chief digital officer.


Here are the rest of this week’s people on the move...

[caption id="attachment_135389" align="alignright" width="150"] Andrew Pemberton[/caption]

Andrew Pemberton was tapped as Pocket Outdoor Media’s new CEO and will succeed Felix Magowan who is moving into the role of co-chairman. Pemberton most recently served as an executive at Panache Cyclewear, but from 2007 to 2009 he served as the publisher of VeloNews when it was owned by Competitor Group Inc., though it is now owned by Pocket Outdoor Media.

Pemberton also previously worked for Magowan as the interactive brand director for Magowan’s company, Inside Communications. “It’s become clear to me that our brands need someone who understands this new media ecosystem and what drives media success,” said Magowan in a statement. “During his 10 years as COO at a successful cycling start-up, [Pemberton] experienced the pressures and challenges facing manufacturers to reach customers in new ways. He is the right person at the right time to lead our media brands.”

Verizon Communications announced that Tim Armstrong is out as CEO of Oath, the parent company of brands like HuffPost and Yahoo, and K. Guru Gowrappan will succeed him. Armstrong, who had served as CEO of AOL prior to the company’s acquisition by Verizon in 2015 and maintained that role after the merger, influenced the 2017 acquisition of Yahoo by Verizon, which were later combined into Oath. He has spent nearly a decade as CEO and his last day will be Oct. 1.  

Gowrappan is a former executive of Alibaba and has served as the COO of Oath since April. He will report to the CEO of Verizon Hans Vestberg, and Armstrong will stay on as an adviser through the end of the year.

[caption id="attachment_135393" align="alignright" width="150"] Erica Smith[/caption]

Erica Smith was tapped as beauty writer for the Cut, where she will be responsible for working alongside the brand’s beauty director Kathleen Hou to expand beauty coverage, including writing about trends, product recommendations and celebrity collaborations. Smith started at the Cut in July as a contributor and before that served as managing editor at Man Repeller.

The Cut president and editor-in-chief Stella Bugbee said in a statement, “Erica brings a fresh voice to the Cut's real-talk beauty coverage,” which will help in the brand in its mission to continue “to cover beauty from the perspective of how real women live and use beauty.”

Koa Beck is stepping down from her role as editor-in-chief of Jezebel and deputy editor Julianne Escobedo Shepherd announced that she will succeed Beck, becoming the fifth EIC in the brand's 11-year history. Prior to joining Jezebel as its culture editor in 2014, Shepherd served as the executive editor of The Fader from 2008 to 2010.

[caption id="attachment_135395" align="alignright" width="150"] Julianne Escobedo Shepherd[/caption]

According to a report from the New York Post, Susie Banikarim, editorial director of Gizmodo Media Group, Jezebel’s parent company, said in a memo to her staff that Beck is leaving the brand in order to finish her book. Beck’s last day is Sept. 28.

Founder and CEO of Bustle Digital Group, Bryan Goldberg, announced in a staff memo, shared by Variety, that the company will relaunch Gawker “in the first half of 2019,” after the domain, social media accounts and archive were acquired by Goldberg for $1.35 million at auction this summer.

In the memo, Goldberg says that Amanda Hale was hired as the site’s publisher and will use her recent experience launching and building The Outline to help to relaunch Gawker.

“She is the right person for this important job,” said Goldberg. “Gawker will be completely distinct from our other properties and sit within a separate corporate subsidiary. That said, it will have access to our shared resources, technology, and business platform.”

[caption id="attachment_135397" align="alignright" width="150"] Dana Wollman[/caption]

Dana Wollman was promoted from executive editor to editor-in-chief of Engadget this week. She joined Engadget over seven years ago as a reporter, and in her new role, Wollman will work to continue expanding the brand in producing both short- and long-form video and in general engagement growth.

“I'm taking over at a busy time,” Wollman wrote in her editor’s letter. “We have some things in the works that I'm excited to share with you guys. Don't worry, though...Our mission remains the same: to explore the ways technology is transforming everyday life.”

Jacob Weisberg, chairman of Slate, announced that he’s leaving the brand after more than two decades with the company. He said in a tweet that his next pursuit will be launching a podcasting audio company with author Malcolm Gladwell.

Parker Molloy is joining Media Matters as its editor-at-large. Most recently, she was a senior staff writer at Upworthy.

Business Insider senior editor Josh Barro announced that he’s leaving the brand to pursue “an exciting next step that will be announced in the coming days.”

Current Detroit bureau chief for Forbes, Joann Muller, is joining Axios to write its Autonomous Vehicles Newsletter. She begins her new role on Oct. 1.

Former Vanity Fair communications manager, Olivia Aylmer, is joining The Wing as a New York events curator starting later this month.

Following last week’s appointment of Sarah Hofstetter to president of comScore, the company announced three further executive hires. Ajay Sravanapudi is taking on the role of SVP of technology architecture and engineering, Kumar Rao will serve as the VP of analytics, and Sumit Shukla was tapped as the new VP of strategic partnerships.

PeopleMost recently, Sravanapudi served as Videology’s chief technology officer where he was responsible for leading technology, product and global support. In his new role, Sravanapudi will work to streamline the company’s product stacks to enable and accelerate true cross-platform measurement.

Rao joins comScore from the Broadcast Audience Research Council where he was the chief of measurement science, and he will focus on leading a team of data scientists to develop new analytical methodologies and solutions in his new position.

Finally, Shukla was most recently the VP of strategic partnerships & corporate development at Neustar and he will be tasked with leading the company’s strategic partnerships team in forging relationships and strategic plans with major digital platforms.

Meredith Corp. has named Scott Macon as president of Synapse Group. Starting on Sept. 17, Macon will take over for Sebastien Bilodeau, who is leaving the company to accept another opportunity. Macon is currently the president of marketing research company Bizrate Insights, which was acquired by Synapse in 2016. He will continue to serve in that capacity in addition to taking on the responsibilities of his new role.

Former SVP of diversity & inclusion at Weber Shandwick, Judith Harrison, was appointed as the 2018-19 president of the New York Women in Communications organization. Harrison began this role on Sept. 1 and will work to increase diversity and inclusion for the organization, while also working with the board of directors to reach all segments of the marketing communications business.

The post SourceMedia Makes Two Additions to Its C-Suite | People on the Move appeared first on Folio:.


          Data Scientist - Proyectos I+D - ECB Engineering Firm - Madrid, España      Cache   Translate Page      
Si te gusta enfrentarte cada día a nuevos retos, ¡eres la persona que necesitamos! Buscamos un perfil Data Scientist con un mínimo de entre 2-3 años de experiencia profesional en proyectos de NLP para unirse al área de innovación en uno de nuestros principales clientes. Tendrás la oportunidad de colaborar en proyectos de tal ámbito, velando por el avance de la investigación en pruebas de concepto y pilotos asociados a cualquier área estratégica de la compañía. Podrás colaborar en...
          Data science hub to replace Illini Hall      Cache   Translate Page      
Illini Hall will be demolished and replaced with a new facility, which will house a data science hub and classrooms for the statistics and mathematics ...
          SQL: Reporting and Analysis for beginners      Cache   Translate Page      

SQL: Reporting and Analysis for beginners, Master SQL for Data Reporting and Daily Data Analysis and gain basic Data Science skills.

The post SQL: Reporting and Analysis for beginners appeared first on Online Classes.


          “And we’re back for Season 6” Paris Machine Learning Newsletter, September 2018 (in French)      Cache   Translate Page      

“And we’re back for Season 6” the Paris Machine Learning Meetup Newsletter, September 2018

Sommaire
  1. L’édito de Franck, Jacqueline, Igor, “And we’re back for Season 6”
  2. On Aime Beaucoup !
  3. La saison dernière.

1 L’édito de Franck, Jacqueline, Igor, “And we’re back for Season 6”

Jacqueline Forien nous rejoint en tant qu’organisatrice du meetup.

La saison 5, c’était 8 hors série et 9 meetups réguliers, plus de 7200+ membres ce qui en fait un des plus grand meetup du monde sur cette thématique. On a vu plein de choses l’année dernière du point de vue politique mais aussi dans les meetups. On reviendra la dessus plus tard dans une autre newsletter. Ce qu’il faut savoir c’est que NIPS la conférence de référence en IA a vendu ses tickets en 11 minutes 38 secondes. D’expérience, c’est plus rapide que la vente des billets de BTS quand il viendront à Bercy en Octobre. Ce qui est sûr c’est que ces expériences que sont les rencontres autour du Machine Learning doivent rester et c’est pour cela que toutes les présentations et vidéos de nos meetups sont dans nos archives et sont listées plus bas dans cette newsletter.

Cette dernière saison n’aurait pas pu se faire sans les entreprises et associations suivantes:
MeritisXebiaDeep AlgoArtefactCap DigitalInvivooFortiaZenikaUrban Linker !LightOnDotAISwissLifeDataiku

Un grand merci pour leur implication dans une communauté dynamique sur l’IA ici à Paris et en Europe.

Notre premier meetup se fera en coordination avec le Women in Machine Learning and Data Science, pour s’inscrire c’est ici: #Hors-série — Paris WiMLDS & Paris ML Meetup

Les dates de nos meetups pour la saison 6:
  • Hors série #1 19/09
  • #2 10/10
  • #3 14/11
  • #4 12/12
  • #5 09/01
  • #6 13/02
  • #7 13/03
  • #8 10/04
  • #9 15/05
  • #10 12/06

Si vous voulez nous accueillir ou sponsoriser, n’hésitez pas à nous contacter grâce à ce formulaire ou via notre site.

Vous pouvez nous suivre sur Twitter @ParisMLgroup.



2. On Aime Beaucoup !

Chloé Azencott, une des speakers du meetup, vient de sortir un livre sur le Machine Learning en Français. C’est Introduction au Machine Learning et il y a plein d’exemples de code.

Des conférences et meetups qu’on aime bien!

++++Important: France is AI conférence: 3e édition de notre conférence annuelle les 17 et 18 octobre 2018 à Station F.+++: Le lien d’inscription eventbriteavec le code promo MEETUPS100 offre 100 place gratuites. Au-delà des 100 premières, les places peuvent être obtenu avec 50% de réduction avec le code MEETUPS50

Les petits nouveaux meetups:

Ceux qui recommencent:

3. La saison dernière


La saison dernière (Saison 5), c’était 8 hors série et 9 meetups réguliers pour un total de 95 meetups en 5 saisons. Voici les liens vers les présentations et videos faites à ces meetups:

Regular meetups

Hors série

Voilà, c’est tout pour aujourd’hui !

Franck , Jacqueline, et Igor.

PS: N’oubliez pas que vous pouvez aussi suivre le Paris Machine Learning Meetup sur Twitter, LinkedIn, Facebook et Google+ .

Vous pouvez consulter les archives des meet ups précédents.

On travaille aussi sur un nouveau site web : MLParis.org

Le Paris Machine Learning Meetup, c’est 7200 membres ce qui en fait un des plus important du monde avec déjà plus de 95 rencontres et 10 dates programmées pour cette saison 6.
  • Si vous êtes étudiant, postdoc ou chercheur, le meet up est une belle tribune pour parler de vos travaux avant de les présenter aux conférences NIPS/ICML/ICLR/COLT/UAI/ACL/KDD ;
  • Pour les startups, c’est un bon moyen de parler de vos projets ou de recruter les futurs superstars de votre équipe IA/Data Science ;
  • Et pour tous, c’est un moyen simple de se tenir informé des derniers développements du domaine et d’avoir des échanges uniques avec les conférenciers et les autres participants.

Comme toujours, premier arrivé, premier entré. Le nombre de places dans les salles est limité. Au delà de leur capacité, nous ne pourrons pas vous faire rentrer. Vous pouvez suivre le taux de remplissage en suivant #MLParis sur twitter.



Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !

          2019 Internship - Bellevue, WA- Data Science - Expedia - Bellevue, WA      Cache   Translate Page      
June 17 – September 6. As a Data Scientist Intern within Expedia Group, you will work with a dynamic teams of product managers and engineers across multiple...
From Expedia - Fri, 31 Aug 2018 21:36:06 GMT - View all Bellevue, WA jobs
          Analyst/Associate, Data Scientist (Investment Analytics and Insights) - CPP Investment Board - Toronto, ON      Cache   Translate Page      
Job Description The Investment Analytics and Insights (IAI) Team is looking for an Analyst/Associate - Data Scientist to incorporate alternative data into...
From CPP Investment Board - Tue, 28 Aug 2018 21:52:27 GMT - View all Toronto, ON jobs
          Principal Market Validation Specialist - PTC - Needham, MA      Cache   Translate Page      
Advance knowledge and experience with Machine Learning / Data Science / Analytics. Customer Satisfaction focus, both internal and external, with strong...
From PTC - Wed, 16 May 2018 14:29:21 GMT - View all Needham, MA jobs
          Data Scientist - Big Data Platform - Trillium Health Partners - Mississauga, ON      Cache   Translate Page      
We will apply advanced analytics and artificial intelligence to uncover new insights to better investigate, innovate and plan but ultimately to improve the...
From Trillium Health Partners - Thu, 13 Sep 2018 17:37:50 GMT - View all Mississauga, ON jobs
          PRACTICAL SQL 2017 Video Training (Delhi)      Cache   Translate Page      
SQL School is one of the best training institutes for Microsoft SQL Server Developer Training, SQL DBA Training, MSBI Training, Power BI Training, Azure Training, Data Science Training, Python Training, Hadoop Training, Tableau Training, Machine Learning ...
          Data Scientist - ZF - Northville, MI      Cache   Translate Page      
Deep Learning, NVIDIA, NLP). You will run cost-effective data dive-ins on complex high volume data from a variety of sources and develop data solutions in close...
From ZF - Thu, 21 Jun 2018 21:14:15 GMT - View all Northville, MI jobs
          Data Scientist - Advanced Analytics - McKinsey & Company - São Paulo, SP      Cache   Translate Page      
You’ll typically work on projects across all industries and functions and will be fully integrated with the rest of our global firm.You’ll also work with...
De McKinsey & Company - Thu, 13 Sep 2018 18:22:48 GMT - Visualizar todas as empregos: São Paulo, SP
          Data Scientist - FedEx Services - Brookfield, WI      Cache   Translate Page      
Women’s Business Enterprise National Council “America’s Top Corporations for Women’s Business Enterprises” - 2016....
From FedEx - Wed, 29 Aug 2018 00:08:19 GMT - View all Brookfield, WI jobs
          Director Data Science (Insights & Analytics) - US Foods - Chicago, IL      Cache   Translate Page      
Excellent business acumen. Business Strategy and Analytics. Experience developing models, monitoring them in production, and measuring their business impact....
From US Foods - Thu, 09 Aug 2018 08:42:46 GMT - View all Chicago, IL jobs
          Artificial Intelligence for the Public Sector: Opportunities and challenges of cross-sector collaboration. (arXiv:1809.04399v1 [cs.AI])      Cache   Translate Page      

Authors: Slava Jankin Mikhaylov, Marc Esteve, Averill Campion

Public sector organisations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high uncertainty environments. The long-term success of data science and AI in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities and challenges from AI for public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations.


          Scientist, Data Science (1 of 2) - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Consideration will be given to Raritan, NJ; Janssen Research &amp; Development LLC, a Johnson &amp; Johnson company, is recruiting for a Scientist, Data Science....
From Johnson & Johnson Family of Companies - Wed, 05 Sep 2018 16:05:17 GMT - View all Spring House, PA jobs
          Senior Analyst, Data Science (1 of 2) - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Consideration will be given to Raritan, NJ; Janssen Research &amp; Development LLC, a Johnson &amp; Johnson company, is recruiting for a Senior Analyst, Data Science....
From Johnson & Johnson Family of Companies - Wed, 05 Sep 2018 04:05:45 GMT - View all Spring House, PA jobs
          Benchmarking and Optimization of Gradient Boosted Decision Tree Algorithms. (arXiv:1809.04559v1 [cs.LG])      Cache   Translate Page      

Authors: Andreea Anghel, Nikolaos Papandreou, Thomas Parnell, Alessandro De Palma, Haralampos Pozidis

Gradient boosted decision trees (GBDTs) have seen widespread adoption in academia, industry and competitive data science due to their state-of-the-art performance in a wide variety of machine learning tasks. In this paper, we present an extensive empirical comparison of XGBoost, LightGBM and CatBoost, three popular GBDT algorithms, to aid the data science practitioner in the choice from the multitude of available implementations. Specifically, we evaluate their behavior on four large-scale datasets with varying shapes, sparsities and learning tasks, in order to evaluate the algorithms' generalization performance, training times (on both CPU and GPU) and their sensitivity to hyper-parameter tuning. In our analysis, we first make use of a distributed grid-search to benchmark the algorithms on fixed configurations, and then employ a state-of-the-art algorithm for Bayesian hyper-parameter optimization to fine-tune the models.


          Data Scientist - ThinkData - Toronto, ON      Cache   Translate Page      
Our platform, Namara, will propel the next wave of intelligent solutions. ThinkData Works Inc....
From ThinkData - Thu, 09 Aug 2018 00:36:56 GMT - View all Toronto, ON jobs
          Data Scientist - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Tue, 05 Jun 2018 16:15:49 GMT - View all New York, NY jobs
          Senior Data Scientist, Zillow Offers - Zillow Group - Seattle, WA      Cache   Translate Page      
Zillow is currently seeking a Senior Data Scientist to lead user acquisition efforts within Zillow Offers team (formerly Instant Offers)....
From Zillow Group - Thu, 13 Sep 2018 01:05:56 GMT - View all Seattle, WA jobs
          Applied Scientist, Economic Data - Intern - Zillow Group - Seattle, WA      Cache   Translate Page      
Zillow Group’s Economic Research team is looking for applied data science interns for the summer of 2019. Zillow Group is owned, fueled and grown by innovators...
From Zillow Group - Wed, 12 Sep 2018 01:06:00 GMT - View all Seattle, WA jobs
          Data Science Intern - Zillow Group - Seattle, WA      Cache   Translate Page      
Each team member focuses on a cross-section of the Zillow Group community. Create data-driven insights to fuel the future of each of Zillow Group’s businesses....
From Zillow Group - Wed, 12 Sep 2018 01:06:00 GMT - View all Seattle, WA jobs
          How to Make Artificial Intelligence Explainable      Cache   Translate Page      

How to Make Artificial Intelligence Explainable

How to Make Artificial Intelligence Explainable - A New Analytic Workbench

FICO today announced the latest version of FICO® Analytics Workbench™, a cloud-based advanced analytics development environment that empowers business users and data scientists with sophisticated, yet easy-to-use, data exploration, visual data wrangling, decision strategy design and machine learning.

As new data privacy regulations shine a spotlight on AI and machine learning, the FICO Analytics Workbench xAI Toolkit helps data scientists better understand the machine learning models behind AI-derived decisions.

“As businesses depend on machine learning models more and more, explanation is critical, particularly in the way that AI-derived decisions impact consumers,” said Jari Koister, vice president of product management at FICO. “Leveraging our more than 60 years of experience in analytics and more than 100 patents filed in machine learning, we are excited at opening up the machine learning black box and making AI explainable. With Analytics Workbench, our customers can gain the insights and transparency needed to support their AI-based decisions.”

How to Make Artificial Intelligence Explainable - Avoiding the Common Pitfalls

“Computers are increasingly a more important part of our lives, and automation is just going to improve over time, so it’s increasingly important to know why these complicated AI and ML systems are making the decisions that they are,” said AI expert and assistant professor of computer science at the University of California Irvine, Sameer Singh. “The more accurate the algorithm, the harder it is to interpret, especially with deep learning. Explanations are important, they can help non-experts to understand the reasons behind the AI decisions, and help avoid common pitfalls of machine learning.”

Built for both business users and data scientists, the FICO® Analytics Workbench™ combines the best elements of FICO’s existing data science tools with several open source technologies, into a single, cloud-ready, machine learning and decision science toolkit, powered by scalable Apache Spark technologies. The Analytics Workbench provides seamless and automated regulatory audit compliance support, producing the necessary documentation for internal review and external regulators.

The Analytics Workbench has been designed for users with a variety of skill sets, from credit risk officers looking for a consistent tool to data scientists and business analysts collaborating and working together to inform and enrich strategic decision making. With an intuitive interface, users can expect faster time-to-value, higher levels of productivity, and significant business improvements through analytically powered decisions.

How to Make Artificial Intelligence Explainable - See Our Demo

For more information, click here.

For a demonstration of analytics workbench, click here.

The post How to Make Artificial Intelligence Explainable appeared first on FICO.


          University receives 2.5 million euros for research in data science      Cache   Translate Page      

Answer market needs

Entitled “ERA Chair in Mathematical Statistics and Data Science for the University of Luxembourg – SanDAL”, this action aims to fill a gap in current research activities to position Luxembourg as a key player in mathematics research. Prof. Jean-Marc Schlenker, Head of the Mathematics Research Unit (RMATH) at the University of Luxembourg explains: “Within a few years only, we have succeeded in building an internationally renowned department in the fields of probability theory, geometry, number theory and mathematical physics. However, with the growing importance of data science in all sectors of the economy and especially in logistics, healthcare, ICT, manufacturing and finance, Luxembourg suffers from a lack of skilled scientists, engineers and researchers. Therefore, this outstanding award will enable us to meet the job market’s needs.”

Develop research and training

The project will boost research and training in mathematical statistics and data science. The ERA Chair research activities will focus on two areas: “High-Dimensional Data Analysis” to have a deeper understanding of emerging computational tools and “New Mathematical Tools for Contemporary Statistics” to develop new and more sophisticated statistical methods. In addition, new teaching programmes at Master and PhD levels will be elaborated to provide future students with the necessary skills in mathematical statistics and data science to understand, develop and apply new tools in various fields. Prof. Stéphane Pallage, Rector of the University of Luxembourg, sees all the benefits of this new funding: “The multidisciplinary ERA Chair will increase the international reputation of the University of Luxembourg by attracting and retaining experienced researchers. Moreover, it will support the competitiveness of the Luxembourg economy by transferring knowledge and know-how”.

Recruitment of an expert team

To ensure the development of a strong and sustainable new direction of research, the University of Luxembourg will recruit highly qualified research staff, namely a permanent professor who will act as the ERA Chair Holder, two postdoctoral researchers and three PhD candidates. Applications for the position of Professor in Mathematical Statistics and Data Science are open until 1 January 2019 via the job portal of the University of Luxembourg.

Figure: overall concept of the SanDAL project

Figure: overall concept of the SanDAL project: research and innovation policy context, Action Plan, ERA Chair and expected impacts

The ERA Chair SanDAL has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 811017.

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          Red Hat Business News      Cache   Translate Page      

read more


          Crisis in the Archives      Cache   Translate Page      

Critics of the executive branch’s information control practices tend to focus on the here and now. They argue that overclassification of national security–related documents undermines democratic self-rule. They inveigh against delays and denials in the implementation of the Freedom of Information Act. They condemn regulations that “incorporate by reference” materials developed by industry groups. They worry about the growing use of black box algorithms, criminal leak investigations, and secret waivers for former lobbyists turned political appointees. All of these critiques raise important issues, even if they sometimes understate the transparency that exists—U.S. administrative agencies “are some of the most extensively monitoredgovernment actors in the world”—or overstate the benefits of sunlight.

One of the executive’s most worrisome information control practices has received relatively little attention, perhaps because it requires taking a longer view. Over the last several decades, as Matthew Connelly explains in a new essay on “State Secrecy, Archival Negligence, and the End of History as We Know It,”[*]our national archives have been quietly falling apart. FOIA backlogs look like a Starbucks queue compared to the 700,000 cubic feet of records at the National Archives and Records Administration’s research facility in Maryland that were unprocessed as of 2013. The Public Interest Declassification Board recently estimated that it would take a year’s work by two million declassifiers to review the amount of data that a single intelligence agency now produces in eighteen months.

The U.S. government’s entire system for organizing, conserving, and revealing the record of its activities, Connelly maintains, is on the verge of collapse; a “digital dark age” awaits us on the other side. His is less a story about excessive information control than a story about the absenceof information control. Archivists simply have not been able to cope with the flood they face. The negative consequences extend far beyond the professional study of history, as Democrats learned last month when NARA announced that it was incapable of reviewing and releasing all of Brett Kavanaugh’s papers before the Senate votes on his nomination to the Supreme Court.

How did this crisis in the archives develop, and what might be done to mitigate it? Woefully inadequate appropriations and “dubious management decisions” bear some of the blame, according to Connelly. When the ratio of spending on the classification and protection of national security secrets to spending on their declassification exceeds 99 to 1, the historical record is bound to suffer. But the deeper cause of the crisis, Connelly suggests, lies in the exponential growth of government records, particularly electronic records. In a world where the State Department generates two billion emails each year—all of which need to be screened for sensitive personal and policy details prior to disclosure through any official process—the traditional tools of archiving cannot possibly keep up.

Maybe the tools ought to be updated for the age of “big data,” then. Connelly has collaborated extensively with data scientists on the problems he highlights, and he argues that sophisticated use of computational methods, from topic modeling to traffic analysis to predictive coding, could go a long way toward rationalizing records management and accelerating declassification. If these techniques were to be combined with bigger budgets for archivists and greater will to curb classification, NARA might one day make good on its aspiration to ensure “continuing access to the essential documentation of the rights of American citizens and the actions of their Government.” There is something intuitively appealing about this vision: Digital technologies got us into this mess, and now they ought to help get us out of it. Connelly’s diagnosis of information overload and political neglect is so stark, however, that one wonders whether any such reforms will prove adequate to the challenge.

Three response pieces recast this challenge in a somewhat different light. The Archivist of the United States, David Ferriero, emphasizes steps NARA is taking to digitize its holdings, enhance public access to them, and enforce government recordkeeping requirements. Ferriero does not dispute that “the country would be well served” by greater funding for the agency he leads, but he suggests that progress is being made even within severe budgetary constraints.

Elizabeth Goitein largely endorses Connelly’s reform proposals but urges that they be pushed further in the area of national security information. Drawing on extensive research and advocacy she has done as co-director of the Brennan Center for Justice’s Liberty and National Security Program, Goitein offers a suite of specific recommendations, from tightening the substantive criteria for classification to requiring federal agencies to spend certain amounts on declassification to subjecting officials who engage in serious overclassification to mandatory penalties.

Finally, Kirsten Weld raises critical questions about Connelly’s characterization of the problem and urges that his reform proposals be pushed much further. Weld points out that the records maintained by NARA represent just a “slice” of U.S. history, albeit an important one, and that the government’s management of that slice has always been bound up with larger political struggles. The true source of the crisis at NARA, Weld submits, is not the rise of electronic records or the politicization of transparency but “the dismantling of the postwar welfare state and the concomitant ascendance of neoliberal governance.” To address the crisis, accordingly, technical fixes are bound to be insufficient. Nothing short of “a sea change in the federal government’s priorities” and “a massive reinvestment in the public sphere” will do.

A crisis in the national archives, all of the authors agree, is a crisis in American democracy. It is certainly not the only one we face, and it may not be the most acute, but preserving a record of our collective history arguably has a kind of epistemic priority. As we fight for our democratic future, these essays remind us to fight for the institutions that help us understand how we arrived at the perilous present.




[*] Connelly’s paper is being published, along with three response pieces, as the sixth installment in a series I am editing for the Knight First Amendment Institute at Columbia University.


          Data Analyst and Tableau Visualization Specialist - Upwork      Cache   Translate Page      
We believe smarter and stronger cities emerge if they aggregate reliable data &
deploy effective data tools so as to inspire astute decision making.

The ideal candidate for this position will be inspired by this initiative, can undertake data analysis, has an enthusiasm for learning new things, and can work in a fast-paced team environment.

Responsibilities and duties:
- Create and edit Tableau dashboards
- Assist to maintain online visualization library
- Create and manipulate data tables within a database environment
- Deliver data tables to other visualization experts upon request
- Research new sources of data products

Qualities:
- Curious
- Diligent
- Creative
- Collaborative – we don’t care who’s right – we care what’s right

Preferred Skills:
- Tableau
- HTML
- Python (numpy, pandas, sklearn, matplotlib, seaborn)
- SQL
- API experience
- Google Analytics
- Javascript
- Data scraping

Posted On: September 13, 2018 19:51 UTC
ID: 214196602
Category: Data Science & Analytics > Data Visualization
Skills: Data Analytics, Data Science, Data Visualization, SQL, Tableau Software
Country: United States
click to apply
          MS in Applied Data Science Online – which track is right for you?      Cache   Translate Page      
At Bay Path University, we'll provide you with a framework for working together regardless of your background and experience. That is why we created two tracks to complete the MS in Applied Data Science degree, which is right for you?
          The Data Science of “Someone Like You” or Sentiment Analysis of Adele’s Songs      Cache   Translate Page      
An extensive analysis of Adele's songs using Natural Language Processing (NLP) on the lyrics, to uncover the underlying emotions and sentiments.
          In Puglia come in Italia servono sempre più informatici      Cache   Translate Page      

Torna in Fiera del Levante la due giorni organizzata da Regione Puglia, InnovaPuglia, Distretto Produttivo dell’Informatica pugliese, in collaborazione con Assinter Italia. Data scientist ed esperti di cyber security: agli ‘Open Days Agenda Digitale 2018’ le competenze necessarie per lo sviluppo del digitale in Italia e Puglia   In Italia c’è sempre più bisogno di […]

L'articolo In Puglia come in Italia servono sempre più informatici sembra essere il primo su Metropoli Notizie.


          Associate Solutions Engineer at CISCO      Cache   Translate Page      
Cisco - The Internet of Everything is a phenomenon driving new opportunities for Cisco and it's transforming our customers' businesses worldwide. We are pioneers and have been since the early days of connectivity. Today, we are building teams that are expanding our technology solutions in the mobile, cloud, security, IT, and big data spaces, including software and consulting services. As Cisco delivers the network that powers the Internet, we are connecting the unconnected. Imagine creating unprecedented disruption. Your revolutionary ideas will impact everything from retail, healthcare, and entertainment, to public and private sectors, and far beyond. Collaborate with like-minded innovators in a fun and flexible culture that has earned Cisco global recognition as a Great Place To Work. With roughly 10 billion connected things in the world now and over 50 billion estimated in the future, your career has exponential possibilities at Cisco.Job Id: 1243269 Location: Lagos, Nigeria Training Location: Prague, Czech Republic Area of Interest: Sales - Services, Solutions, Customer Success Job Type: New Graduate Start date: 28th July, 2019. What You'll Do You'll be part of our Cisco Sales Associates Program (CSAP), an award-winning graduate training program for young talent aspiring to move into sales or engineering roles. For the first months of the program you'll learn about the latest technology advancements and how to position Cisco's architectures, solutions and products to our customers. During the second part of your CSAP year, you'll move into an engineering role as part of your on-the-job-experience within the Global Virtual Engineering (GVE) Team. You will be actively involved in sales opportunities and assigned to specific projects that align to your skill set. The program, while challenging, will push you to become the best version of yourself. You'll be encouraged to pursue industry-standard certifications and be assessed and coached through customer simulations and on-the-job activities. We'll offer you a safe and fun environment to practice what you've learnt, all the while providing you with feedback to develop your potential. Thanks to this rigourous training plan, we've earned a strong reputation within our internal sales organization. GVE is a multilevel technical presales organization, that provides software and systems engineering services to customers, partners, and internal Cisco sales employees. Upon graduating from the program, you'll be a Virtual Systems Engineer (VSE) where you'll ultimately accelerate your career into a Systems Engineer role and beyond. As a VSE you'll engage with our customers and partners as a trusted technology advisor. You'll work with Account Managers and together you'll position the benefits of our Cisco solutions to your customer, using our market-leading collaboration tools. Who You'll Work With You'll train alongside incredibly talented individuals, like yourself, from different countries and diverse backgrounds. Early on, you'll make long-lasting friendships and belong to a rich human network that will support you through out your career. As a successful Associate Solutions Engineer (ASE), you'll expand your software and networking knowledge to collaborate with Cisco sales professionals and provide technical solutions for our customers and partners. You'll learn from top experts and coaches in a unique classroom setting where we use our own 'state-of-the-art' collaboration technology. You'll have your own mentor, a CSAP alumnus who's been in your shoes and will guide you in your first year. With a strong Cisco team committed to your success, you'll gain hands-on education and experience, while receiving an attractive salary and pursuing your career aspirations. Who You Are Technology enthusiast, who enjoys talking about innovation and always keeps up with the latest technology news. A strong communicator with the confidence to engage and talk to a wide range of people. View team collaboration as instrumental to achieving success. Enjoy looking at practical real life challenges and thinking creatively to solve them. Approach situations with an open and curious mind, taking on challenges with an eye for opportunity. What You Need To Be Eligible Graduate by October 2018. Graduate from a relevant technical degree such as; Computer Science, Computer Engineering, Software Engineering, Electronics Engineering, Telecommunications Engineering, Cyber Security, Information Technology, Mathematics, Physics, Informatics, Data Science or similar. Fluent in English. Hold the right to live and work in the country that you are applying for, without future company sponsorship required. Student visas and temporary permits obtained on your own will not be acceptable. Willing to relocate for 12 months of training to hub. Visa assistance and relocation package to training hub will be provided as required. Willing to return to your country you applied for, unless otherwise required due to business needs. Knowledge and experience in software languages. C, C++, C#, Java or Python are desired. Summary of Job Locations Training location: Prague, Czech Republic for first 12 months Location after training: Lagos, Nigeria
          IBM Event: Scaling AI for the Enterprise      Cache   Translate Page      
Copyright © 2018 http://jtonedm.com James TaylorShadi Copty discussed one IDE and one runtime for AI and data science across the enterprise as part of IBM’s AI approach. Shadi identified three major trends that are impacting data science and ML: Diversity of skillsets and workflows with demand remaining high and new approaches like deep learning posing additional [...]
          DATA ENGINEER (DIGITAL AND DATA SCIENCE) - The Globe and Mail - Toronto, ON      Cache   Translate Page      
DATA ENGINEER (DIGITAL AND DATA SCIENCE) POSITION CODE: 2018-133 LOCATION: The Globe and Mail, Toronto SALARY: Commensurate with qualifications and...
From The Globe and Mail - Tue, 28 Aug 2018 03:17:59 GMT - View all Toronto, ON jobs
          #46 Cinco dirigentes de Ciudadanos con curriculums dudosos       Cache   Translate Page      

#45

A ver, creo que no me he explicado bien.

Yo no soy un acomplejado autodidacta que está jugando a la lingüistica para subplir sus complejos. Yo me gano muy bien la vida en mi sector y estoy muy feliz con lo que he conseguido, por lo que me parece genial que le llameis como queráis a los títulos que os otorgan después de participar en procesos regulados de obtención del conocimiento. Por eso no entiendo eso del "no cuela". ¿No cuela el que? Si yo lo que quiero es entender como debo expresar ciertos conceptos en Español de forma clara y concisa.

Mi problema es que cuando hablo en inglés, por temas culturales, ellos tienen dos grupos de palabras separados para agrupar a las personas que realizan unas tareas usando unos conocimientos, y para describir los títulos oficiales que tienen las personas, otorgados en procesos regulados de obtención del conocimiento.

Eso me permite expresar de forma clara y concisa ideas como "Regarding software engineers, not all of them have a degree in computer science, others went through a degree in Maths or Physics, and some of them are self-taught"

Yo quiero expresar que las personas que tienen los conocimientos necesarios para resolver problemas de ingenio relacionados con el conocimiento que rodea a las ciencias de la computación y el desarrollo de software, no todos ellos vienen de una formación reglada superior de ciencias de la computación, sino de otras areas, como matematicas o física, o incluso son autodidactas.

Pero claro, queda super largo.

Voy a intentar acortarlo:

"Los ingenieros de software, no todos ellos tienen título de ingenieria informatica, algunos lo tienen en matematicas o fisica, y otros son autodidactas"

pero claro, si han estudiado mates o física, no son ingenieros. Y si son autodidactas, tampoco son ingenieros, por lo que mi frase es un oximoron en Español, y no se puede construir así, por que hemos acoplado la palabra ingenieria a la titulación en si, algo completamente innecesario, que lleva a confusiones o a dificultades comunicativas :-)

Por cierto, tu ejemplo no sirve, técnico en España es también una titulación :-)

por cierto, otro apunte, aparte de que técnico es también una titulación en España, normalmente no necesitas un cientifico de datos, necesitas un ingeniero de datos.

Un cientifico es quien intenta incrementar el estado del arte conocido sobre un asunto. Un ingeniero aplica el estado del arte conocido de forma creativa para resolver problemas concretos.

Tu mismo ibas a escribir ingeniero de datos (data engineer), pero has escrito cientifico de datos (data scientist), expresando incorrectamente lo que querías, por que no te lo permitía la palabra "ingeniero", acoplada al título :-)

» autor: jcarlosn


          2019 Internship - Bellevue, WA- Data Science - Expedia - Bellevue, WA      Cache   Translate Page      
June 17 – September 6. As a Data Scientist Intern within Expedia Group, you will work with a dynamic teams of product managers and engineers across multiple...
From Expedia - Fri, 31 Aug 2018 21:36:06 GMT - View all Bellevue, WA jobs
          Asean Foundation & SAP Gelar Kompetisi ADSE, Ini Pemenangnya      Cache   Translate Page      
Asean Foundation dan SAP mengadakan kompetisi Asean Data Science Explorer (ADSE) yang dimenangi Tim OWL dari Universinas Bina Nusantara (Binus) dan berhasil menjadi juara pada tingkat nasional.
          DATA ENGINEER (DIGITAL AND DATA SCIENCE) - The Globe and Mail - Toronto, ON      Cache   Translate Page      
DATA ENGINEER (DIGITAL AND DATA SCIENCE) POSITION CODE: 2018-133 LOCATION: The Globe and Mail, Toronto SALARY: Commensurate with qualifications and...
From The Globe and Mail - Tue, 28 Aug 2018 03:17:59 GMT - View all Toronto, ON jobs
          Lead Educator, Data Science - BrainStation - Vancouver, BC      Cache   Translate Page      
Strong command of querying and programming languages (Python, SQL, R), and visualization tools (Tableau, Python packages), as well as experience applying...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Vancouver, BC jobs
          Educator, Data Science - BrainStation - Toronto, ON      Cache   Translate Page      
Strong command of querying and programming languages (SQL, Python, R), and visualization tools (Tableau, Python packages, etc.), as well as experience applying...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Toronto, ON jobs
          Associate Educator, Data Science - BrainStation - Toronto, ON      Cache   Translate Page      
Strong command of querying and programming languages (SQL, Python, R) and visualization tools (Tableau, Python packages, etc.), as well as experience applying...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Toronto, ON jobs
          Associate Educator, Data Science - BrainStation - Vancouver, BC      Cache   Translate Page      
Strong command of querying and programming languages (Python, SQL, R), and visualization tools (Tableau, Python packages), as well as experience applying...
From Brainstation - Wed, 12 Sep 2018 01:52:06 GMT - View all Vancouver, BC jobs
          Data Scientist - Innovate BC (formerly BC Innovation Council) - Vancouver, BC      Cache   Translate Page      
Simulation, Numerical Modeling. About Innovate BC (formerly BC Innovation Council)*.... $75,000 - $85,000 a year
From Indeed - Mon, 10 Sep 2018 15:48:10 GMT - View all Vancouver, BC jobs
          PROJECT ORIENTED Video REALTIME TRAINING ON SQL DBA 2017 (Delhi)      Cache   Translate Page      
SQL School is one of the best training institutes for Microsoft SQL Server Developer Training, SQL DBA Training, MSBI Training, Power BI Training, Azure Training, Data Science Training, Python Training, Hadoop Training, Tableau Training, Machine Learning ...
          Software Development Principal Engineer – Data Scientist - DELL - Austin, TX      Cache   Translate Page      
Learn more about Diversity and Inclusion at Dell here. Selecting features, building and optimizing classifiers using machine learning techniques....
From Dell - Sat, 07 Jul 2018 11:22:08 GMT - View all Austin, TX jobs
          Data Scientist - Game Hive - Toronto, ON      Cache   Translate Page      
Our most popular games include the Tap Titans, Beat the Boss and Antrim Escape series (and many, more!). Game Hive is building a new generation of casual mobile...
From Indeed - Wed, 15 Aug 2018 13:34:49 GMT - View all Toronto, ON jobs
          Data Science Software Developer - Genetec - Montréal, QC      Cache   Translate Page      
Whether it be our gym, our health conferences, our games lounge (PS3, foosball table, pool table, Ping-Pong table), our on-site Bistro offering delectable,...
From Genetec - Wed, 20 Jun 2018 18:06:25 GMT - View all Montréal, QC jobs
          Data Scientist - Genetec - Montréal, QC      Cache   Translate Page      
Whether it be our gym, our health conferences, our games lounge (PS3, foosball table, pool table, Ping-Pong table), our on-site Bistro offering delectable,...
From Genetec - Wed, 20 Jun 2018 18:06:28 GMT - View all Montréal, QC jobs
          Principal / Senior Software Engineer - RichRelevance - Seattle, WA      Cache   Translate Page      
(Crunch, Cassandra, HBase, Hive, Presto, no-SQL databases). Principal / Senior Software Engineer - Data Science Engineering....
From RichRelevance - Sun, 27 May 2018 09:38:05 GMT - View all Seattle, WA jobs
          Senior Data Scientist, Zillow Offers - Zillow Group - Seattle, WA      Cache   Translate Page      
You have worked with gigantic data sets and are comfortable accessing those data (even if they’re in JSON) with Hive and Presto....
From Zillow Group - Thu, 13 Sep 2018 01:05:56 GMT - View all Seattle, WA jobs
          Data Science Intern - Zillow Group - Seattle, WA      Cache   Translate Page      
Interest in working with multi-terabyte-sized data sets and are comfortable accessing those data (even if they're in JSON format) with Hive and Presto....
From Zillow Group - Wed, 12 Sep 2018 01:06:00 GMT - View all Seattle, WA jobs
          Senior Software Engineer, Cloud Engineering - ExtraHop Networks, Inc. - Seattle, WA      Cache   Translate Page      
Experience with data science information processing pipeline (Spark / Presto / SQL / Hadoop / HBASE). Big Data, the cloud, elastic computing, SaaS, AWS, BYOD,...
From ExtraHop Networks, Inc. - Tue, 11 Sep 2018 18:44:58 GMT - View all Seattle, WA jobs
          Data Scientist, Zillow Offers - Zillow Group - Seattle, WA      Cache   Translate Page      
Dive into Zillow's internal and third party data (think Hive, Presto, SQL Server, Python, R, Tableau) to make strategic recommendations (e.g., improve...
From Zillow Group - Thu, 06 Sep 2018 01:06:28 GMT - View all Seattle, WA jobs
          Data Scientist - Hudson - Singapore      Cache   Translate Page      
R1105253 | Hudson Global Resources (Singapore) Pte Ltd | EA Licence #:. Major Multi-nations Company in Singapore....
From Hudson - Fri, 07 Sep 2018 11:10:03 GMT - View all Singapore jobs
          (USA-CA-San Francisco) Regulatory Analytics & Research Data Scientist      Cache   Translate Page      
**Company** Based in San Francisco, Pacific Gas and Electric Company, a subsidiary of PG&E Corporation (NYSE:PCG), is one of the largest combined natural gas and electric utilities in the United States. And we deliver some of the nation's cleanest energy to our customers in Northern and Central California. For PG&E, **Together, Building a Better California** is not just a slogan. It's the very core of our mission and the scale by which we measure our success. We know that the nearly 16 million people who do business with our company count on our more than 24,000 employees for far more than the delivery of utility services. They, along with every citizen of the state we call home, also expect PG&E to help improve their quality of life, the economic vitality of their communities, and the prospect for a better future fueled by clean, safe, reliable and affordable energy. Pacific Gas and Electric Company is an Affirmative Action and Equal Employment Opportunity employer that actively pursues and hires a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, national origin, ancestry, sex, age, religion, physical or mental disability status, medical condition, protected veteran status, marital status, pregnancy, sexual orientation, gender, gender identity, gender expression, genetic information or any other factor that is not related to the job. **Department Overview** The Regulatory Analytics and Research department are regulatory professionals who support the operating lines of business by providing regulatory expertise; managing the development, approval and implementation of regulatory filings, rates and tariffs and advocating the business needs to our regulators **Position Summary** The Data Scientist prepares technical content in support of regulatory proceedings and rate cases that involves conducting research, developing data analytics, assisting witnesses and supporting case management process. The Data Scientist designs, builds and maintains data ingestion pipelines, data access solutions/ databases, complex analytics and reports/ visualization to generate actionable insights for revenue requirements, sales forecast and research, cost of service and rate making (electric and gas pricing). The Data Scientist will report to the Senior Manager of Regulatory Analytics & Research. **Job Responsibilities** + Design and build data ingestion pipelines required for optimal extraction, transformation, and loading of data from a wide variety of data sources. + Design and build large and complex datasets that meet functional and non-functional business requirements. + Optimize data storage and query performance; ensure data integrity, cleanliness, and availability; and document data sources, methodologies and test plans/ results. + Build analytics, visualization and dashboards to provide actionable insights and key business metrics. + Identify, design, and implement process improvements by automating and integrating manual processes for greater efficiency and scalability. + Collaborate with stakeholders across organizations to support their data analytics needs. + Support IT with product evaluation and benchmarking for future infrastructure needs and projects. **Qualifications** Minimum: + Bachelor’s degree in Computer Science, Engineering or related field, + Two years of applied data engineering and analytics experience Desired Qualifications + Experience in SQL (Teradata) to extract, store and analyze large datasets. + Experience using data visualization/BI tools such as Tableau + Hands-on programming in Python using big data technologies- AWS (EC2), Hadoop (Hive) and Spark. + Experience implementing data science functions in R and Python. + Familiarity in Base SAS, SAS Grid and SAS Enterprise Guide. + Familiarity in load research and forecast functions and regulatory rate making. + Familiarity in Oracle Utilities Customer Care and Billing, SAP/BW and Salesforce.
          Descartes Labs to Help DARPA Build Geospatial Data Repository      Cache   Translate Page      
The Defense Advanced Research Projects Agency has chosen Descartes Labs to build a technology platform designed to store geospatial imagery and machine learning tools for data analysis. Descartes Labs said Wednesday it will provide the cloud-based infrastructure for the agency to create  applications as part of the Geospatial Cloud Analytics program and offer the apps to data scientists via a […]
          Heroes of Deep Learning: Top Takeaways for Aspiring Data Scientists from Andrew Ng’s Interview Series      Cache   Translate Page      
Introduction Andrew Ng is the most recognizable personality of the modern deep learning world. His machine learning course is cited as the starting point ... The post Heroes of Deep Learning: Top...

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          How Machine Learning Algorithms & Hardware Power Apple’s Latest Watch and iPhones      Cache   Translate Page      
Introduction This is a great time to be a data scientist – all the top tech giants are integrating machine learning into their flagship ... The post How Machine Learning Algorithms &...

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          Data Scientist - Mosaic North America - Jacksonville, FL      Cache   Translate Page      
Overview The Data Scientist will be a part of a team chartered to merge and mine large amounts of retail execution, sales and other relevant data to develop...
From Mosaic North America - Fri, 06 Jul 2018 20:26:15 GMT - View all Jacksonville, FL jobs
          Data Scientist Lead - Mosaic North America - Jacksonville, FL      Cache   Translate Page      
Data Scientist Lead - (DataSciLe2090718) Description Overview: The Data Scientist will be a part of a team chartered to merge and mine large amounts of...
From Mosaic North America - Fri, 07 Sep 2018 18:33:08 GMT - View all Jacksonville, FL jobs
          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page      
Virtual machine switching); Large scale distributed systems, real-time data analysis, machine learning, windows internals (networking stack and other OS...
From Microsoft - Thu, 09 Aug 2018 04:41:50 GMT - View all Redmond, WA jobs
          Software Development Engineer - Video Advertising (Data Science & Analytics) - Amazon.com - Seattle, WA      Cache   Translate Page      
As a founding team member, you’ll contribute to crucial choices regarding technology selection and architecture....
From Amazon.com - Thu, 02 Aug 2018 07:20:18 GMT - View all Seattle, WA jobs
          Scientist, Data Lead - Mosaic North America - Jacksonville, FL      Cache   Translate Page      
Overview The Data Scientist will be a part of a team chartered to merge and mine large amounts of retail execution, sales and other relevant data to develop...
From Mosaic North America - Wed, 22 Aug 2018 14:26:26 GMT - View all Jacksonville, FL jobs
          Sr. Data Scientist - Life Sciences - Health Catalyst - Salt Lake City, UT      Cache   Translate Page      
Machine learning experience required. The role has great potential for a successful candidate as they will form the initial seed of a new business unit with...
From Health Catalyst - Tue, 11 Sep 2018 02:31:12 GMT - View all Salt Lake City, UT jobs
          Data Scientist - Game Hive - Toronto, ON      Cache   Translate Page      
Our most popular games include the Tap Titans, Beat the Boss and Antrim Escape series (and many, more!). Game Hive is building a new generation of casual mobile...
From Indeed - Wed, 15 Aug 2018 13:34:49 GMT - View all Toronto, ON jobs
          Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Knowledge and experience on applying statistical and machine learning techniques on real business data....
From Lincoln Financial Group - Mon, 27 Aug 2018 18:47:26 GMT - View all Boston, MA jobs
          Sr. Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Implements and maintains predictive and statistical models to identify business opportunities and solve complex business problems....
From Lincoln Financial Group - Fri, 17 Aug 2018 18:34:07 GMT - View all Boston, MA jobs
          Senior/Data Analyst (Data Science Team) - M1 Limited - Jurong      Cache   Translate Page      
Job ID: IS-SDA-DS-4 Job Type: Full Time Job Classification: Senior / Executive Department: Information Systems Function: Information Systems Location: Jurong...
From M1 Limited - Tue, 17 Jul 2018 06:37:27 GMT - View all Jurong jobs
          Data Scientist - FedEx Services - Brookfield, WI      Cache   Translate Page      
Women’s Business Enterprise National Council “America’s Top Corporations for Women’s Business Enterprises” - 2016....
From FedEx - Wed, 29 Aug 2018 00:08:19 GMT - View all Brookfield, WI jobs
          Design, Data Science, And The Algorithms That Run Our Lives      Cache   Translate Page      
We encounter algorithms every day in many different experiences — from YouTube to Facebook to Uber. Personalization and individualization (impossible to do without algorithms) are hailed as keys to the experiences of the future and form the centerpiece of the very best and most seamless experiences. And computing capabilities and services make this more common […]
          Data Scientist - FedEx Services - Brookfield, WI      Cache   Translate Page      
Women’s Business Enterprise National Council “America’s Top Corporations for Women’s Business Enterprises” - 2016....
From FedEx - Wed, 29 Aug 2018 00:08:19 GMT - View all Brookfield, WI jobs
          Principal Technologist - Machine Learning and Data Science - Blue Origin - Kent, WA      Cache   Translate Page      
While in this role, you will leverage your extensive experience in machine learning and data science to accelerate and innovate across business areas to drive...
From Blue Origin - Wed, 13 Jun 2018 05:31:45 GMT - View all Kent, WA jobs
          Data Scientist Job - SAIC - Reston, VA      Cache   Translate Page      
Work will be performed primarily with internal company contacts. Determines the appropriate analytics based on the data and the desired outcomes, using...
From SAIC - Sat, 08 Sep 2018 02:46:43 GMT - View all Reston, VA jobs
          Data Scientist - Imbellus - Vancouver, BC      Cache   Translate Page      
Masters in Data Science, Statistics, Psychology or related quantitative field. At Imbellus, we build technology-enabled assessments that evaluate how people...
From Imbellus - Fri, 27 Jul 2018 00:29:03 GMT - View all Vancouver, BC jobs


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