Capital One
Principal Associate, Data Scientist - Card Intelligence
$162K–$201K
McLean, VA +2 · Principal · Full-time · Posted Aug 7, 2026
- Location
- McLean, VA
New York, NY
Cambridge, MA - Experience
- 5 years with a quantitative Bachelor's degree; 3 years with a quantitative Master's degree or quantitatively concentrated MBA; no years specified with a quantitative PhD.
- Education
- Quantitative Bachelor's, Master's, or PhD; a quantitative MBA concentration is an alternative to a Master's degree.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization.
About the job
Build and deploy machine learning models across the credit card lifecycle, using large customer datasets to inform business strategy and improve cardholder experiences.
What you'll do
- Partner with data scientists, software engineers, and product managers to deliver customer-facing products.
- Develop models through design, training, evaluation, validation, and implementation.
- Analyze large volumes of numeric and textual data and communicate insights in business terms.
What they ask for
- One of: a quantitative Bachelor's degree plus 5 years of data analytics experience; a quantitative Master's degree or quantitatively concentrated MBA plus 3 years of data analytics experience; or a quantitative PhD.
- Degree must be obtained on or before the scheduled start date if currently in progress.
Nice to have
- At least 1 year of AWS experience; at least 3 years in Python, Scala, or R; at least 3 years in machine learning; and at least 3 years in SQL.
- STEM Master's degree plus 3 years of data analytics experience, or a STEM PhD.
Pay
$161,800 - $184,600 USD base salary (Cambridge, MA); $161,800 - $184,600 USD base salary (McLean, VA); $176,500 - $201,400 USD base salary (New York, NY)
Tools
Python, Conda, AWS, H2O, Spark, Scala, R, SQL
AI focus
Develop and deploy machine learning models across the credit card lifecycle, including marketing, acquisitions, underwriting, and fraud prevention; work may involve clustering, classification, sentiment analysis, time series, and deep learning.
Model-written summary · the original posting is authoritative