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The Principal ML Research Scientist will also have a hands-on roll and is expected to customize and create various machine learning algorithms to operate over multi-domain data and optimizing the performance of those algorithms on the data.
$122,200 - $220,900 a yearExpandApply NowActive JobUpdated 13 days ago - UpvoteDownvoteShare Job
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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.
Full-timeExpandApply NowActive JobUpdated 13 days ago - UpvoteDownvoteShare Job
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The expected hiring range for a Senior Machine Learning Scientist I is $145,400 -$196,70 depending on skills, experience, education, and training; relevant licensure / certifications; performance history; and work location.
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At least 4 years of hands-on experience in several of the following areas: machine learning, statistics, optimization, regression modelling, deep learning, generative AI, causal inference, experimentation.
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The Principal NLP Research Scientist will also have a hands-on roll and is expected to customize and create various machine learning algorithms to operate over multi-domain data and optimizing the performance of those algorithms on the data.
ExpandApply NowActive JobUpdated 14 days ago - UpvoteDownvoteShare Job
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You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. Our areas of research include reinforcement learning, recommender systems, causal inference, and deep learning with a focus on transformers.
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3+ years of hands-on experience in neural network development, with a strong focus on computer vision and/or natural language processing. Experience in other machine learning domains, such as audio/signal processing or synthetic aperture radar (SAR.
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This position will primarily involve hands-on data science activities, including evaluation of data quality, data management, data visualization and data analysis, and the building machine learning models, using observational data, to support drug development.
Full-timeExpandApply NowActive JobUpdated 8 days ago - UpvoteDownvoteShare Job
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Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation. At least 1 year of experience with machine learning.
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Our ideal candidate is a collaborative and innovative problem solver, who is profoundly hands-on and is passionate about applying state-of-the-art solution frameworks and machine learning techniques to develop behavioral segments, audience propensities and other data products that embed strategic audience insights into Gannett’s daily decisions and value chains.
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Team Description The Card Fraud Data Science team builds the machine learning models that help protect our customers and Capital One against fraudsters. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.
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Demonstrated experience deploying machine learning models on multimedia data, to include joint text, audio , video, hardware, and peripherals. Demonstrated experience employing machine learning and deep learning modules such as Pandas, Scikit, Tensorflow, Pytorch.
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Hands-on experience with machine learning libraries such as TensorFlow, Keras, or Scikit-learn. Join Accenture Federal Services to do the work you love in an inclusive, collaborative, and caring community, where you can be empowered to grow, learn and thrive through hands-on experience, certifications, industry training and more.
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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. At least 4 years' experience with custom code-driven machine learning projects using packages such as XGBoost, pySpark, H2O.
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The Compliance Risk Management Analytics and Innovation team builds the machine learning models that contribute to the organizations regulatory compliance and risk management program. Empower testing for compliance with state and federal regulations using AI and Machine Learning techniques.
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