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The EMS Division is the home to the NSF IUCRC Center of Big Learning (CBL), which focuses on deep learning acceleration, model compression, image and video compression, remote sensing, and vision.
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Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.
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You will be impacting every aspect of the business and it will require various techniques, from diving deep into data with SQL and Big Data tools, all the way to deploying modern machine learning models.
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Build, validate, test, and deploy machine learning or other data science models to predict and influence the behavior of consumers with the goal of optimizing products & processes.
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Successful candidates will have a broad and deep knowledge of machine learning and deep learning, the ability to map models into scalable, production models, the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers, and be able to take an iterative approach to tackle big, long term problems.
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Experience combining user research and data science methodologies across multiple products and teams. Our ultimate goal is to enable and increase the use of data in the company through creative approaches and the implementation of powerful resources such as operational and analytical databases, queue systems, BI tools, and data science technology.
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As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.
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11 West 19th Street (22008), United States of America, New York, New YorkManager, Data Science - Fraud, Deep LearningManager, Data Scientist, Fraud. The Fraud Data Science team builds the machine learning models that help protect our customers and Capital One against fraudsters.
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At least 2 years’ experience with Pytorch, Tensorflow or other Deep Learning frameworks. At least 2 years’ experience building advanced Deep Learning architectures (for example: RNN’s, CNN’s, Transformers.
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The Card Fraud Data Science team builds the machine learning models that help protect our customers and Capital One against fraudsters. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
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They offer deep technical knowledge in areas like machine learning, statistical modeling, data analysis, and big data processing. Director of Data ScienceLocation: Tysons Corner, VAClearance: US CitizenIntegral Federal is hiring a Director of Data Science to support program evaluations, conducts opportunity technical analysis, and identifies and aligns initiatives to broader business goals and objectives.
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Build and deploy scalable data science algorithms by applying statistical analytics, Machine Learning (ML), Anomaly detection, and other techniques to enable supply planning. Knowledge of common tools for data storage and processing (e.g. Spark, Hadoop Map/Reduce, Hive, Cassandra) including drilling into problems of running large scale software in a big network.
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Experience with big data technologies (e.g., Hadoop, Spark, Databricks, Snowflake) and cloud platforms (e.g., AWS, Azure). Strong proficiency in statistical analysis and modeling techniques, such as linear regression, logistic regression, decision trees, random forests, clustering, deep learning algorithms, boosting, text mining and NLP.
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Contribute to the growth of the Data Science team by sharing your ideas, intellectual property and learning from others. We employ scalable cutting-edge machine learning (Client), deep learning (DL), and Natural Language Processing (NLP) knowledge to better target customers and prospects, understand and personalize the content, and context needed to optimize their book-listening experience.
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Experience in NLP, computer vision, deep learning techniques. Collaborate with cross-functional teams including data science, software engineering, and product management to build predictive models, forecasting tools, and recommendation systems.
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