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As Software Architect, Data Scientist, Full-Stack Developer, Machine Learning Engineer, Artificial Intelligence Engineer, ML/AI Engineering Leader, AI Software Engineering Leader, ML Software Development Leader, ML/AI Engineering Manager.
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Experience working in Senior, Lead, Principal, Manager, and Director level roles in relevant positions such as Software Architect, Data Scientist, Machine Learning Engineer, etc.
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As a data scientist, you’re excited at the prospect of unlocking the secrets held by a data set, and you’re fascinated by the possibilities presented by IoT, machine learning, and artificial intelligence.
$93,300 - $212,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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As a data scientist, you can help turn these complex data sets into useful information to solve global challenges. Data Scientist, Mid. Experience with distributed data and computing tools, including MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL.
$73,100 - $166,000 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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The successful candidate will bring experience as a data scientist and machine learning leader working for a company where "AI" is considered core to the company's success. Strong experience with Python/R, forecasting methodologies, machine learning and statistics.
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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 Today - UpvoteDownvoteShare Job
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Uses descriptive statistics, predictive analytics, machine learning, and other methods to learn about and derive insights from the patterns and relations within a dataset. The Data Scientist provides data science and advanced analytical support, technical expertise, and thought leadership to generate actionable insights from data.
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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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Experience using tools such as Azure’s Machine Learning and Cognitive Services; AWS’s SageMaker, Polly and Rekognition; DataRobot; or H2O.ai. Apply the latest Computer Vision and Machine Learning algorithms to build customer-centric solutions.
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Collaborate on a team of data scientists to build machine learning models through all phases of development, from design through training, evaluation, validation, implementation, and maintenance.
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Build models and automated tools using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks.
$111,600 - $212,800 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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As a Healthcare IT Data Scientist, you will play a vital role in leveraging data analytics and machine learning to drive actionable insights and innovations that enhance patient care, improve healthcare operations, and support strategic decision-making.
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Experience with Machine Learning, Artificial Intelligence, or Natural Language Processing. 3+ years of experience with data science, machine learning, cloud engineering, data analytics, or data research.
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Health Data ScientistThe Opportunity: As a data scientist, you’re excited at the prospect of unlocking the secrets held by a data set, and you’re fascinated by the possibilities presented by IoT, machine learning, and artificial intelligence.
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In this role as a data scientist on the Credit Tools team, you’ll be optimizing valuations models for high-speed performance to enable faster decision-making, conducting comprehensive deep dives into machine learning pipelines and our data ecosystem, and leveraging distributed computing frameworks to build tools and solutions that work seamlessly with terabytes of data across multiple platforms.
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