Capital One
Manager, Data Scientist - Model Risk Office
$179K–$225K
McLean, VA +2 · Manager · Full-time · Posted Jan 13, 2026
- Location
- McLean, VA
Richmond, VA
Chicago, IL - Experience
- Data analytics: 6 years with a bachelor’s degree, 4 years with a master’s degree or quantitative MBA, or 1 year with a PhD. Also requires at least 1 year each in open-source programming for large-scale analysis, machine learning, and relational or vector databases.
- Education
- Bachelor’s, master’s, or PhD in a quantitative field; alternatively, an MBA with a quantitative concentration at the master’s-level route.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization.
About the job
Validate enterprise models, build challenger machine learning models, and assess and communicate model risks, including risks associated with Generative AI.
What you'll do
- Partner with data scientists, software engineers, and product managers to identify and quantify model risks.
- Build challenger machine learning models and contribute to the model governance framework.
- Validate models across business domains and present business impacts of identified risks to executives.
What they ask for
- Quantitative bachelor’s degree plus 6 years, master’s degree or quantitative MBA plus 4 years, or quantitative PhD plus 1 year of data analytics experience.
- At least 1 year using open-source programming languages for large-scale data analysis.
- At least 1 year working with machine learning.
- At least 1 year using relational or vector databases.
Nice to have
- PhD in a STEM field plus 3 years of data analytics experience; at least 1 year working with AWS.
- At least 4 years using Python, Scala, or R for large-scale data analysis; or 4 years in machine learning, including GenAI, or building or validating models in specified risk domains.
Pay
USD $179,400 - $204,700 base salary (Chicago, IL); USD $197,300 - $225,100 base salary (McLean, VA); USD $179,400 - $204,700 base salary (Richmond, VA)
Tools
PyTorch, Hugging Face, LangChain, Vector Databases, LLMOps, Python, Scala, R
AI focus
Assess risks associated with Generative AI and use machine learning technologies, including GenAI, in model validation and challenger modeling.
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