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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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We focus on research and development of new technologies within the domain of Artificial Intelligence with a focus on Embeddings and Foundation Models. In Emerging ML, you will work at all phases of the data science lifecycle, including:Build machine learning models through all phases of development, from design through training, evaluation and validation, and partner with engineering teams to operationalize them in scalable and resilient production systems that serve 50+ million customers.
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Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR.
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We are seeking a Post-Doctoral Research Scientist with a background in Artificial Intelligence, Deep Learning, and Machine Learning to join our Breeding Technologies team in the Seed Product Development group working on Artificial Intelligence in Breeding at our global headquarters in Indianapolis, Indiana location.
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Emerging ML is the data science and machine learning team inside Capital One’s Applied Research organization. Technical: You have hands-on experience developing data science solutions from concept to production using open source tools and modern cloud computing platforms.
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Build machine learning models through all phases of development, from design through training, evaluation and validation, and partner with engineering teams to operationalize them in scalable and resilient production systems that serve 50+ million customers.
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Team DescriptionEmerging ML is the data science and machine learning team inside Capital One's Applied Research organization. You have experience with clustering, classification, sentiment analysis, time series analysis and deep learning.
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Perform advanced data analytics tasks such as simulation, optimization, artificial intelligence and machine learning, advanced statistical analysis, regression, data science, decision analysis, performance metric development, risk assessment, scheduling, process improvement, and process automation.
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Knowledge, Skills, Abilities and (Technical) Competencies Thought leadership in the external data science/artificial intelligence/machine learning community of practice, as demonstrated through peer-reviewed journal publications, intellectual property/patent achievements, conference presentations, volunteering in professional organizations for the advancement of the field, participation in externally sponsored research projects, open source contributions, or similar activities.
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