Capital One
Director, Data Scientist - US Card
$269K–$335K
McLean, VA +2 · Director · Full-time · Posted Oct 26, 2026
- Location
- McLean, VA
New York, NY
Cambridge, MA - Experience
- Data analytics: 9 years with a quantitative bachelor's degree, 7 years with a quantitative master's degree or quantitatively concentrated MBA, or 4 years with a quantitative PhD. Also, at least 4 years each in open-source programming for large-scale data analysis, machine learning, and relational databases.
- Education
- Quantitative bachelor's, master's, or PhD; a quantitatively concentrated MBA is an alternative to the master's degree.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Leads data science work for US Card, developing and deploying machine-learning models across the credit-card lifecycle to shape business strategy and customer experiences.
What you'll do
- Partner with data scientists, software engineers, and product managers to deliver customer-facing products.
- Build machine-learning models from design through training, evaluation, validation, and implementation.
- Translate analytical insights into business goals and impact.
What they ask for
- A quantitative bachelor's degree plus 9 years of data analytics experience; or a quantitative master's degree or quantitatively concentrated MBA plus 7 years; or a quantitative PhD plus 4 years.
- At least 4 years leveraging open-source programming languages for large-scale data analysis.
- At least 4 years working with machine learning.
- At least 4 years utilizing relational databases.
Nice to have
- At least 1 year of experience working with AWS.
- At least 3 years of experience managing people.
Pay
$269,100 - $307,200 USD base salary (Cambridge, MA); $269,100 - $307,200 USD base salary (McLean, VA); $293,600 - $335,100 USD base salary (New York, NY)
Tools
Python, Conda, AWS, H2O, Spark, Scala, R
AI focus
Develops and deploys machine-learning models for credit-card marketing, acquisitions, underwriting, and fraud prevention.
Model-written summary · the original posting is authoritative