Lead Data Scientist & Manager — ML, Big Data

Capital One; McLean (VA); Hybrid; USD 190,000 - 230,000; Full time

Benefits offered by this job

Performance-based incentives

Comprehensive benefits

Job summary

Capital One is seeking a Data Scientist to advance machine learning solutions across billions of records. You will collaborate with data scientists, software engineers, and product managers to design and deploy data products that drive customer value and business outcomes. A strong foundation in statistics, ML, and data processing is essential.

The role emphasizes open‑source tooling, cloud platforms, and translating technical work into actionable business decisions for scalable impact.

Qualifications

• Bachelor’s degree in a quantitative field plus 6 years analytics experience.
• Master’s degree in quantitative field or MBA with quantitative concentration plus 4 years analytics.
• PhD in quantitative field plus 1 year analytics.

Responsibilities

• Partner with cross‑functional teams to deliver a data product.
• Build machine learning models from design to deployment.
• Leverage open‑source tools and cloud computing platforms to analyze large data sets.
• Translate complex analyses into tangible business decisions.

Skills

Customer focus

Innovation

Creativity

Leadership

Technical skills

Statistical analysis

Big data

Education

Bachelor’s Degree in quantitative field + 6 years analytics

Master’s Degree in quantitative field or MBA with quantitative concentration + 4 years analytics

PhD in quantitative field + 1 year analytics

Tools

Python

Conda

AWS

H2O

Spark

Job description

Capital One is seeking a Data Scientist to advance machine learning solutions across billions of records. You will collaborate with data scientists, software engineers, and product managers to design and deploy data products that drive customer value and business outcomes. A strong foundation in statistics, ML, and data processing is essential.

The role emphasizes open‑source tooling, cloud platforms, and translating technical work into actionable business decisions for scalable impact.