# Senior Director, Data Science at Capital One

AI Level 2, AI centrality 43 out of 100. McLean, VA.

## Details

- Company: [Capital One](https://jobsbylevel.com/companies/capital-one)
- AI level: AI Level 2 (score 43 out of 100)
- Location: McLean, VA
- Posted: October 7, 2026
- Apply: https://jobsbylevel.com/go/7d63c377-e107-489c-b4ee-2cff6d398660

## Description

Senior Director, Data Science 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 Science Leader 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. About the Team We're building a portfolio of agentic AI products to transform Capital One's engineering organizations and lines of business – autonomous coding agents, context engineering, and agentic model-development lifecycle to name a few. We're creating a new science team to help define the long-term strategy, analyze & optimize impact throughout the ecosystem, and drive business value at enterprise scale. As a scientist on the team, you'd design AI/ML models and help build the solutions behind these products. You'd also own the science that guides them: designing experiments (A/B tests), modeling telemetry, and turning the results into AI strategy and guidance for C-suite leadership. In this role, you will: Partner with a cross-functional team of software engineers, distinguished researchers, and solutions architects to drive great decisions through modeling Define and drive towards an end state that is based on simplicity and the adoption of digital technologies, cloud hosting, and open source software. Distill the details of complex interconnected modeling systems to influence senior business leaders on model strategy, business use, and risks Assess, challenge, and at times defend state-of-the-art decision-making systems to internal and regulatory partners Shape our practice of building machine learning models, from design through training, evaluation, validation, and implementation Oversee development of benchmark and challenger models to stress test critical modeling decisions The Ideal Candidate is: A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. 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. Technical. You’re comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. 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. Basic Qualifications: 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 11 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 9 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) plus 6 years of experience performing data analytics At least 6 years of experience

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