Capital One
Manager, Data Science - AI Foundations
$197K–$246K
New York, NY +2 · Manager · Full-time · Posted Sep 4, 2026
- Location
- New York, NY
McLean, VA
San Jose, CA - Experience
- Data analytics: 6 years with a bachelor's degree, 4 years with a master's degree or quantitatively concentrated MBA, or 1 year with a PhD. 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, or an MBA with a quantitative concentration (master's-level route).
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Leads development and deployment of scalable AI/ML and NLP solutions, including LLM-powered customer experiences for Capital One’s mobile app.
What you'll do
- Partner with data science, engineering, and product teams to deliver AI-powered customer products.
- Design, train, evaluate, and validate machine learning and NLP models, then operationalize them in scalable production systems.
- Adapt and fine-tune language models for customer-facing applications and communicate technical findings in business terms.
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 utilizing relational databases.
Nice to have
- PhD in a STEM field.
- Experience with AWS; 4+ years in Python, Scala, or R; 4+ years with machine learning; or 4+ years with SQL.
Pay
$197,300 - $225,100 USD base salary (McLean, VA); $215,200 - $245,600 USD base salary (New York, NY); $215,200 - $245,600 USD base salary (San Jose, CA)
Tools
Pytorch, AWS Ultraclusters, Hugging Face, LangChain, Lightning, VectorDBs, Python, SQL
AI focus
Develops AI/ML and NLP applications, adapting and fine-tuning LLMs; the posting also describes training language or large computer vision models and mentions training optimization, self-supervised learning, explainability, and RLHF.
Model-written summary · the original posting is authoritative