Capital One
Manager, Data Scientist - Alternative Data Strategy
$179K–$246K
New York, NY +3 · Manager · Full-time · Posted Oct 21, 2026
- Location
- New York, NY
McLean, VA
Richmond, VA
Chicago, IL - Experience
- Bachelor's: 6 years; master's or MBA with quantitative concentration: 4 years; PhD: 1 year, all in data analytics. Also requires at least 1 year 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; a master's-level alternative is an MBA with a quantitative concentration.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Evaluate and integrate alternative datasets into US Card credit strategies, building features, models, and scalable data systems to improve credit decisioning.
What you'll do
- Evaluate emerging data vendors and products, engineer features, and build scalable processing and monitoring systems.
- Build and validate machine learning models, including challenger models, and guide users on dataset applications.
- Collaborate cross-functionally and translate technical findings into business goals.
What they ask for
- Quantitative-field degree and data analytics experience: bachelor's plus 6 years, master's or quantitatively focused MBA plus 4 years, or PhD plus 1 year.
- 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 databases.
Nice to have
- At least 1 year working with AWS.
- At least 4 years in Python, Scala, or R for large-scale data analysis; machine learning; or SQL.
Pay
$179,400 - $204,700 USD base salary (Chicago, IL); $197,300 - $225,100 USD base salary (McLean, VA); $215,200 - $245,600 USD base salary (New York, NY); $179,400 - $204,700 USD base salary (Richmond, VA)
Tools
Python, Conda, AWS, H2O, Spark, Scala, R, SQL
AI focus
Applies machine learning to credit decisioning, including evaluating vendor models and building challenger models with alternative data.
Model-written summary · the original posting is authoritative