Capital One
Manager, Data Science
$197K–$246K
McLean, VA +2 · Manager · Full-time · Posted Sep 30, 2026
- Location
- McLean, VA
New York, NY
San Jose, CA - Experience
- Bachelor’s degree: 6 years; master’s degree or quantitatively focused MBA: 4 years; PhD: 1 year, all in data analytics. Also requires at least 1 year each in open-source programming languages for large-scale data analysis, machine learning, and relational databases.
- Education
- Bachelor’s, master’s, or PhD in a quantitative field; alternatively, an MBA with a quantitative concentration at the master’s level.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Leads data science work developing and deploying large-scale machine-learning models and personalized recommendation experiences for Capital One’s web and mobile applications.
What you'll do
- Partner with data scientists, software engineers, and product managers to deliver customer-facing products.
- Build machine-learning models through design, training, evaluation, validation, and implementation.
- Analyze large volumes of numeric and textual data and communicate results in terms of 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
- PhD in a STEM field plus 3 years of data analytics experience.
- At least 4 years of experience with Python, Scala, or R for large-scale data analysis; machine learning; or SQL, or at least 1 year with AWS.
Pay
McLean, VA: $197,300 - $225,100 USD base salary; New York, NY: $215,200 - $245,600 USD base salary; San Jose, CA: $215,200 - $245,600 USD base salary
Tools
Python, Conda, AWS, H2O, Spark, Scala, R, SQL
AI focus
Develops high-scale personalized recommendation engines, including work involving Foundation Models, Reinforcement Learning, Causal Inference, Transformer-based architectures, and Recommender Systems.
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