Capital One
Manager, Data Scientist - Card Mainstreet
$179K–$246K
McLean, VA +2 · Manager · Full-time · Posted Sep 10, 2026
- Location
- McLean, VA
New York, NY
Richmond, VA - Experience
- Data analytics: 6 years with a bachelor's degree, 4 years with a master's degree or quantitatively focused MBA, or 1 year with a PhD. Also at least 1 year each in open-source programming for large-scale data analysis, machine learning, and relational databases.
- Education
- Bachelor's, master's, or PhD in a quantitative field; alternatively, an MBA with a quantitative concentration at the master's-level path. 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
Leads data science work to build and implement machine-learning models and deliver customer-focused products using large-scale data.
What you'll do
- Partner with data scientists, software engineers, and product managers to deliver products.
- Develop machine-learning models from design through training, evaluation, validation, and implementation.
- Translate technical findings into business goals.
What they ask for
- Quantitative degree and data-analytics experience: bachelor's plus 6 years, master's or quantitatively focused MBA plus 4 years, or PhD plus 1 year.
- At least 1 year using open-source programming languages for large-scale data analysis.
- At least 1 year working with machine learning.
- At least 1 year using relational databases.
Nice to have
- PhD in a STEM field plus 3 years of data analytics experience; at least 1 year working with AWS.
- At least 4 years in Python, Scala, or R for large-scale data analysis; machine learning; or SQL.
Pay
McLean, VA: $197,300 - $225,100 USD base salary; New York, NY: $215,200 - $245,600 USD base salary; Richmond, VA: $179,400 - $204,700 USD base salary
Tools
Python, Conda, AWS, H2O, Spark, Scala, R, SQL
AI focus
Build machine-learning models, including development, training, evaluation, validation, and implementation; stated methods include clustering, classification, sentiment analysis, time series, and deep learning.
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