Capital One
Senior Manager, Data Science - Model Risk
$209K–$286K
Richmond, VA +3 · Manager · Full-time · Posted Oct 30, 2026
- Location
- Richmond, VA
McLean, VA
Chicago, IL
New York, NY - Experience
- 2–7 years of data analytics experience depending on degree; at least 2 years each in open-source programming for large-scale analysis, machine learning, and relational databases.
- Education
- Quantitative bachelor's, master's, or PhD; an MBA with a quantitative concentration may substitute for a master's degree. Degree must be obtained 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 model-risk data science work, building and validating machine-learning models and translating results into business decisions.
What you'll do
- Partner with data scientists, software engineers, and product managers to deliver products.
- Develop machine-learning models from design and training through evaluation, validation, and implementation.
- Communicate technical findings in terms of business goals and help improve decision-making.
What they ask for
- Quantitative bachelor's degree plus 7 years of data analytics experience; or quantitative master's degree or quantitatively focused MBA plus 5 years; or quantitative PhD plus 2 years.
- 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/or managing people.
- At least 5 years using Python, Scala, or R for large-scale data analysis and/or working with machine learning.
Pay
USD $209,000 - $238,500 base salary (Chicago, IL); USD $229,900 - $262,400 base salary (McLean, VA); USD $250,800 - $286,200 base salary (New York, NY); USD $209,000 - $238,500 base salary (Richmond, VA)
Tools
Python, Conda, AWS, H2O, Spark, Scala, R
AI focus
Build, evaluate, validate, and implement machine-learning models, including work with clustering, classification, sentiment analysis, time series, and deep learning.
Model-written summary · the original posting is authoritative