Capital One
Manager, Data Science - AI Foundations
$193K–$241K
New York, NY +2 · Manager · Full-time · Posted Oct 27, 2025
- Location
- New York, NY
McLean, VA
San Jose, CA - Experience
- 6 years with a bachelor's degree; 4 years with a master's degree or quantitatively concentrated MBA; or 1 year with a PhD, all performing 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; alternatively, an MBA with a quantitative concentration under the stated experience pathway.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Leads development and production deployment of scalable NLP and machine-learning solutions, including fine-tuned language models, for customer-facing Capital One products.
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 help operationalize them in scalable production systems.
- Apply NLP and large language models to customer-facing applications 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 concentrated MBA plus 4 years, or PhD plus 1 year.
- At least 1 year of experience using open-source programming languages for large-scale data analysis.
- At least 1 year of experience working with machine learning.
- At least 1 year of experience utilizing relational databases.
Nice to have
- Experience working with AWS.
- At least 4 years’ experience in Python, Scala, or R; machine learning; or SQL.
Pay
$193,400 - $220,700 USD base salary; $211,000 - $240,800 USD base salary
Tools
Pytorch, AWS Ultraclusters, Hugging Face, LangChain, Lightning, VectorDBs, AWS, SQL
AI focus
Build and deploy scalable AI/ML products, with emphasis on NLP, adapting and fine-tuning LLMs, and training, evaluating, and validating models for customer-facing applications.
Model-written summary · the original posting is authoritative