Capital One
Manager, Data Science - Emerging ML
$197K–$246K
McLean, VA +2 · Manager · Full-time · Posted Jun 30, 2026
- Location
- McLean, VA
New York, NY
San Jose, CA - Experience
- Degree-dependent data analytics experience: 6 years with a bachelor's, 4 years with a master's or quantitative MBA, or 1 year with a PhD; plus at least 1 year each in open-source programming for large-scale data analysis, machine learning, and relational databases.
- Education
- Quantitative bachelor's, master's, or PhD; alternatively, an MBA with a quantitative concentration is listed with the master's pathway.
- Visa sponsorship
- Will consider sponsoring a new qualified applicant for employment authorization.
About the job
Conduct research and develop machine learning models, including embeddings and foundation-model applications, then partner with engineering and business teams to deploy them in scalable systems and improve customer and business outcomes.
What you'll do
- Research self-supervised learning, transformer models, and representation learning; develop customer behavioral models.
- Build, evaluate, and validate models throughout the data science lifecycle and work with engineering to operationalize them.
- Analyze large-scale numerical and textual data and run experiments with product and business teams.
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 leveraging 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
- Experience working with AWS.
- At least 4 years’ experience in Python, Scala, or R; machine learning; or SQL.
Pay
USD $197,300 - $225,100 base salary (McLean, VA); USD $215,200 - $245,600 base salary (New York, NY); USD $215,200 - $245,600 base salary (San Jose, CA)
Tools
Python, Scala, Spark, AWS, R, SQL
AI focus
Research and develop AI/ML technologies focused on embeddings and foundation models, including self-supervised learning, transformers, representation learning, and encoder/decoder integration.
Model-written summary · the original posting is authoritative