Principal Data Scientist

Capital One • McLean, VA

1d

About The Position

Principal Data Scientist Principal Associate, Data Scientist - AI in Credit 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 the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making. 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. Team Description The US Card Intelligence Segments organization builds industry-leading machine learning models that empower core underwriting decisions. We partner closely with a wide range of job families - machine learning engineers, product managers, credit analysts and data engineers - to deliver solutions from ideation to implementation. The AI in Credit team is developing the next generation of predictive models to forecast how customers use their Capital One cards. We are a cross-functional collaboration that spans across the entire company, with scientists and engineers specializing in applied credit, deep learning, and high performance computing. Join us as we push the boundary of data-driven credit decisioning. Role Description In this role, you will: Develop modeling approaches to forecast credit outcomes in a rapidly changing economic environment Apply cutting-edge explainability techniques to identify insights from complex models Develop frameworks for AI-model-based decisioning in a highly regulated industry Collaborate on a cross-functional team with a wide range of specializations and experience The Ideal Candidate is: A data guru. “Big data” doesn’t faze you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science. Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea. Statistically-minded. You’ve built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning. Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.

Requirements

• Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics
• A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 3 years of experience performing data analytics
• A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)

Nice To Haves

• Master’s Degree in an applied research field (physics/astronomy, chemistry, biology, engineering, operations research, sociology, etc.) plus 3 years of experience in data science, or PhD in an applied research field
• Experience in explainability a big plus
• At least 3 years’ experience in Python, Scala, or R
• At least 3 years’ experience with machine learning

Responsibilities

• Develop modeling approaches to forecast credit outcomes in a rapidly changing economic environment
• Apply cutting-edge explainability techniques to identify insights from complex models
• Develop frameworks for AI-model-based decisioning in a highly regulated industry
• Collaborate on a cross-functional team with a wide range of specializations and experience

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees