Data Science Manager — ML & Impact Leader

capitalone; McLean (VA); On-site; USD 197,000 - 225,000; Full time

Job summary

Capital One is seeking a Manager, Data Science to lead a cross-functional team in delivering data-driven products at scale. You will apply Python, AWS, and modern ML tooling to transform vast datasets into actionable insights across the card business.

The role emphasizes collaboration with software engineers, product managers, and data scientists to deploy production-ready models and communicate outcomes to non-technical stakeholders.

Qualifications

• Bachelor's degree in a quantitative field plus 6 years of analytics experience.
• Master's degree in quantitative field or MBA with a quantitative concentration plus 4 years analytics.
• PhD in a quantitative field plus 1 year of analytics experience.
• At least 1 year of experience leveraging open source programming languages for large scale data analysis.
• At least 1 year of experience working with machine learning.
• At least 1 year of experience utilizing relational databases.

Responsibilities

• Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
• Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
• Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals

Skills

Python

Open source tools

Machine learning

Relational databases

Education

Bachelor's degree in quantitative field

Master's degree in quantitative field or MBA with quantitative concentration

PhD in quantitative field

Tools

AWS

H2O

Spark

Conda

Job description

Capital One is seeking a Manager, Data Science to lead a cross-functional team in delivering data-driven products at scale. You will apply Python, AWS, and modern ML tooling to transform vast datasets into actionable insights across the card business.

The role emphasizes collaboration with software engineers, product managers, and data scientists to deploy production-ready models and communicate outcomes to non-technical stakeholders.