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As a Staff Machine Learning Research Scientist you will work on some of the deepest problems in machine learning. Hands-on experience with machine learning and NLP tools and libraries from Numpy to PyTorch.
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Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets. Spalding Consulting, Inc. is seeking a Senior Data Scientist in Lexington Park, MD.
ExpandApply NowActive JobUpdated 7 days ago - UpvoteDownvoteShare Job
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MS and PhD candidates must have completed graduate work related to machine learning, computer vision, or robotics. Apply state-of-the-art machine learning software solutions for various artificial intelligent system applications.
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The Principal Data Scientist is a highly motivated and experienced individual proficient in advanced machine learning models, particularly language models, document understanding and analysis, and code generation.
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5+ years of hands on experience with ML tools such as R, Python, Spark, SageMaker, TensorFlow, or similar- Experience in machine learning and statistical techniques such as classification, clustering, regression, statistical inference, collaborative filtering, and natural language processing, experimental design, social networking analysis, feature engineering etc.
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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.
ExpandApply NowActive JobUpdated 9 days ago - UpvoteDownvoteShare Job
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Able to carry on with data conversion from different sourcesAbility to use Microsoft Power BINotions of Machine learning algorithmsProblem-solving aptitudeExcellent communication and presentation skills.
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Partners with diverse Microsoft teams to identify and explore opportunities for the novel application of machine learning (ML) and other data-science tools to above industry. Has deep mathematical understanding of statistics and machine learning.
$133,600 - $256,800 a yearFull-timeExpandApply NowActive JobUpdated 4 days ago - UpvoteDownvoteShare Job
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Machine Learning Engineer/Data Scientist. Machine Learning Engineer/Data Scientist. Good working knowledge of Cloud platforms (GCP preferred), and Airflow DAG development. In this unique position, individual will be at the intersection on Client Engineering and Data Science, and perform tasks to support our advanced measurement products.
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GliaCell’s Enterprise capabilities include Full-Stack Application Development, Big Data, Cloud Technologies, Analytics, Machine Learning, AI, and DevOps Containerization. GliaCell is seeking a Cryptanalytic Computer Scientist on one of our subcontracts.
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In this role, you will be part of a team working in a fast-paced, open-ended environment with cutting edge technologies in deep learning and generative AI. You’ll help define and influence how our firm’s data scientists and business teams leverage data and machine learning to improve our processes and products.
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We are currently seeking a Data Scientist who will create strategy, implement and execute the strategy and use technologies such as Machine Learning, Artificial Intelligence, and Data Analytics.
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The ideal candidate will be adept in navigating the data stack and able to support initiatives in all facets from analytics/data engineering and product analytics to machine learning. We’re innovating in the fast-paced world of live auctions in categories including sports, fashion, video games, and streetwear.
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We are tasked with finding a Senior Machine Learning Engineer to join their expanding team. One of the founders and the Chief Scientist are lecturers at Stanford including for one of the most coveted courses.
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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. Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation.
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