Capital One
Director, Data Scientist - External Data Strategy
$269K–$335K
McLean, VA +1 · Director · Full-time · Posted Jan 6, 2026
- Location
- McLean, VA
New York, NY - Experience
- Degree-dependent data analytics experience: 9 years with a bachelor’s, 7 years with a master’s or quantitative MBA, or 4 years with a PhD; plus at least 4 years each in open-source programming for large-scale analysis, machine learning, and relational databases.
- Education
- Bachelor’s, master’s, or PhD in a quantitative field; alternatively, an MBA with a quantitative concentration.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Leads the External Data Strategy team in building data pipelines, analytics, and machine-learning models that support customer and business decisions.
What you'll do
- Lead 10–15 data scientists, setting strategy, providing technical guidance, and developing team members.
- Partner with data scientists, engineers, and product managers to build data, insights, machine-learning models, and pipelines through deployment.
- Develop expertise in consumer financial data and communicate technical work as business goals.
What they ask for
- Bachelor’s in a quantitative field plus 9 years of data analytics experience; or master’s in a quantitative field or quantitative MBA plus 7 years; or PhD in a quantitative field plus 4 years.
- At least 4 years using open-source programming languages for large-scale data analysis.
- At least 4 years working with machine learning.
- At least 4 years using relational databases.
Nice to have
- At least 3 years working with AWS.
- At least 3 years managing people; also lists 5 years of Python for large-scale data analysis, machine learning, or Spark as preferred.
Pay
$269,100 - $307,200 USD base salary; $293,600 - $335,100 USD base salary
Tools
Python, Conda, AWS, Databricks, Spark, SQL
AI focus
Build machine-learning models and data pipelines, including design, training, evaluation, validation, and implementation.
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