Capital One
Principal Associate, Data Scientist - Applied AI
$162K–$201K
New York, NY +1 · Principal · Full-time · Posted Sep 25, 2026
- Location
- New York, NY
McLean, VA - Experience
- Bachelor's: 5 years performing data analytics; master's or MBA with quantitative concentration: 3 years; PhD: no years specified. Preferred experience includes the durations listed under preferred qualifications.
- Education
- Bachelor's, master's, or PhD in a quantitative field; alternatively, an MBA with a quantitative concentration at the master's route. Degree must be obtained by the scheduled start date.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Build and deploy enterprise Generative AI solutions, including multi-agent systems and advanced RAG pipelines, for Commercial Bank business processes.
What you'll do
- Partner with data scientists, machine learning engineers, and product managers to deliver Generative AI solutions.
- Build multi-agent systems and RAG pipelines using numeric and textual data.
- Develop Generative AI models and pipelines from context engineering through fine-tuning, evaluation, validation, and deployment.
What they ask for
- Quantitative-field degree and qualifying data analytics experience: bachelor's plus 5 years, master's or quantitatively concentrated MBA plus 3 years, or PhD.
- Hands-on machine learning and AI experience through professional work or rigorous academic research.
- Experience building, validating, and backtesting statistical models; knowledge of techniques including classification, clustering, sentiment analysis, time series, and deep learning.
Nice to have
- At least 2 years in AI, Information Retrieval, Deep Learning or NLP; at least 3 years in Python, Scala, or R.
- At least 3 years in machine learning and at least 3 years with SQL; or the stated STEM master's plus 3 years in data analytics or STEM PhD.
Pay
USD $161,800 - $184,600 base salary; USD $176,500 - $201,400 base salary
Tools
Python, LangChain, AWS, Workflows, Vector Databases, Scala, R, SQL
AI focus
Build enterprise Generative AI solutions, including multi-agent systems and advanced RAG; develop, fine-tune, evaluate, validate, and deploy models and pipelines.
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