Capital One
Manager, Data Scientist - Recommendation & Personalization Systems
$197K–$246K
McLean, VA +2 · Manager · Full-time · Posted Sep 30, 2026
- Location
- McLean, VA
New York, NY
San Jose, CA - Experience
- Data analytics: 6 years with a bachelor’s, 4 years with a master’s or quantitatively concentrated MBA, or 1 year with a PhD. Additionally, at least 1 year each in open-source 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 to build and deploy personalized recommendation systems for Capital One’s web and mobile applications using large-scale machine learning.
What you'll do
- Partner with data scientists, software engineers, and product managers to deliver customer-facing products.
- Build machine learning models from design through training, evaluation, validation, and implementation.
- Analyze large volumes of numeric and textual data and translate findings into business goals.
What they ask for
- Quantitative-field degree and data analytics experience: bachelor’s plus 6 years, master’s or quantitatively concentrated 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 in Python, Scala, or R, machine learning, or SQL; or at least 1 year with AWS.
Pay
$197,300 - $225,100 USD base salary; $215,200 - $245,600 USD base salary
Tools
Python, Conda, AWS, H2O, Spark, Scala, R, SQL
AI focus
Researches and deploys personalized recommendation systems, including work with Foundation Models, Reinforcement Learning, Causal Inference, Transformer-based architectures, and recommender systems.
Model-written summary · the original posting is authoritative