Capital One
Principal Associate, Data Scientist - Emerging ML
$162K–$201K
McLean, VA +2 · Principal · Full-time · Posted Jul 28, 2026
- Location
- McLean, VA
New York, NY
San Jose, CA - Experience
- 5 years of data analytics experience with a bachelor's degree; 3 years with a master's degree or quantitatively concentrated MBA; no years specified for a PhD. Preferred: at least 3 years in Python, Scala, or R; machine learning; and SQL.
- Education
- Quantitative-field bachelor's degree, master's degree, quantitatively concentrated MBA, or PhD, as specified in the experience alternatives.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Develop and productionize machine learning solutions, especially in embeddings and foundation models, and run experiments that improve customer experiences and business outcomes.
What you'll do
- Design, train, evaluate, and validate machine learning models, then partner with engineering to deploy them in scalable production systems.
- Work with business and product teams on experiments across marketing, servicing, and fraud prevention.
- Write software to collect, explore, visualize, and analyze large numerical and textual datasets.
What they ask for
- Bachelor’s degree in a quantitative field plus 5 years of data analytics experience; or a master’s in a quantitative field or quantitatively concentrated MBA plus 3 years; or a PhD in a quantitative field.
- For the degree route used, the required degree must be obtained by the scheduled start date.
Nice to have
- At least 3 years of experience in Python, Scala, or R.
- At least 3 years of experience with machine learning and at least 3 years with SQL.
Pay
USD $161,800 - $184,600 base salary (McLean, VA); USD $176,500 - $201,400 base salary (New York, NY); USD $176,500 - $201,400 base salary (San Jose, CA)
Tools
Python, Scala, Spark, AWS, R, SQL
AI focus
Research and develop AI technologies focused on embeddings and foundation models; build and productionize machine learning models.
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