Capital One
Senior Manager, Data Science - Business Card and Payment
$230K–$262K
McLean, VA · Manager · Full-time · Posted Oct 19, 2026
- Experience
- Data analytics: 7 years with a bachelor's, 5 years with a master's or quantitatively focused MBA, or 2 years with a PhD. Also requires 2 years each in open-source programming for large-scale data analysis, machine learning, and relational databases.
- Education
- Bachelor's, master's, or PhD in a quantitative field; master's alternative includes an MBA with a quantitative concentration. Degree may be in progress if expected by the scheduled start date.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Leads data science work that builds and implements machine-learning models and decision systems for small-business card underwriting and marketing.
What you'll do
- Partner with data scientists, software engineers, and product managers to deliver products and improve business decisions.
- Build machine-learning models through design, training, evaluation, validation, and implementation.
- Translate analytical findings into business goals and contribute to team leadership and talent development.
What they ask for
- Quantitative degree (or in progress) with one of these experience paths: bachelor's plus 7 years, master's or quantitatively focused MBA plus 5 years, or PhD plus 2 years in data analytics.
- At least 2 years using open-source programming languages for large-scale data analysis.
- At least 2 years working with machine learning.
- At least 2 years using relational databases.
Nice to have
- At least 1 year working with AWS and at least 1 year managing people.
- At least 5 years in Python, Scala, or R for large-scale data analysis; econometrics or causal inference; or machine learning, specifically reinforcement learning.
Pay
$229,900 - $262,400 USD base salary
Tools
Python, Conda, AWS, H2O, Spark, Scala, R
AI focus
Build, validate, and implement machine-learning models and decision systems, including work with classification, clustering, sentiment analysis, time series, deep learning, and reinforcement learning.
Model-written summary · the original posting is authoritative