Manager, Data Science - AI Foundations
Not Disclosed•Full-TimeOn-site
location_onNYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and relational databases in 1988. Fast-forward a few years, and this innovation and our passion for data have skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.
The AI Foundations Specialist Models Data Science team builds and ships state-of-the-art scalable architecture and AI/ML solutions for Capital One's award-winning mobile app. We partner with product, tech, and design teams to deliver app features that delight customers with dynamic and personalized experiences, enable them to chat with Capital One's digital assistant Eno, or search for useful content. You will be the driving force to experiment, innovate, and create next-generation experiences powered by the latest emerging generative AI technologies.
As a Data Scientist at Capital One, you'll be part of a team leading the next wave of disruption at a whole new scale. You will operate across billions of customer records to unlock big opportunities that help everyday people save money, time, and agony in their financial lives. In this role, you will partner with a cross-functional team to deliver AI-powered products that change how customers interact with their money. You will leverage a broad stack of technologies to reveal insights hidden within huge volumes of numeric and textual data, serving as the expert in Natural Language Processing (NLP) to harness the power of Large Language Models (LLMs). You will build machine learning and NLP models through all phases of development, from design through training, evaluation, and validation, partnering with engineering teams to operationalize them in scalable and resilient production systems that serve 80+ million customers.
We are looking for a customer-first leader who loves the process of analyzing and creating, sharing a passion to do the right thing. You will be innovative, continually researching and evaluating emerging technologies to stay current on state-of-the-art methods. You will thrive on bringing definition to big, undefined problems, asking questions, and pushing hard to find answers. As a leader, you will challenge conventional thinking, work with stakeholders to identify and improve the status quo, and be passionate about talent development. You will be influential, bringing a cross-functional team along in breakthrough innovations and communicating clearly to share findings with non-technical audiences.
Capital One is committed to a fair and inclusive hiring process. We consider qualified applicants regardless of background. If you require an accommodation during the application or interview process, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information provided will be kept confidential.
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. We promote a drug-free workplace and will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws.
Work model: On-site
NYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
New York, New York
PhD in a STEM field (Science, Technology, Engineering, or Mathematics). Experience working with AWS. At least 4 years of experience in Python, Scala, or R. At least 4 years of experience with machine learning. At least 4 years of experience with SQL.
Skills: Statistical Modeling, Relational Database, Machine Learning, Pytorch, Aws, Hugging Face, Langchain, Lightning, Vectordbs, Natural Language Processing.
Education: Bachelor's in a quantitative field with 6 years of experience; Master's in a quantitative field or MBA with quantitative concentration plus 4 years of experience; PhD in a quantitative field plus 1 year of experience.