Data Science Manager: ML & Analytics Lead

Capital One; New York (NY); On-site; USD 215,000 - 246,000; Full time

Job summary

Capital One is seeking a Data Scientist to join the Commercial Business Analysis (CBA) and CODIA teams. You will partner with data scientists, analysts, and relationship managers to deliver data-driven value for clients and the bank.

The role covers building and validating models, modernizing data infrastructure, and supporting long-term technical integrations across multiple business projects, including risk assessment and portfolio management.

Qualifications

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

Responsibilities

• Partner with cross-functional team to deliver a product creating value for clients.
• Collaborate with enterprise AI teams to build ML models through all development phases.
• Conduct rigorous statistical analysis and back-test against third-party data.
• Modernize data infrastructure with analytical tools on core data schemas and real-time validation.
• Support long-term technical integrations for complex business projects including run-off analysis and underwriting automation.

Skills

Statistics

Machine learning

Relational databases

Open-source programming

Python/Scala/R

Education

Bachelor's in quantitative field

Master's in quantitative field or MBA

PhD in quantitative field

Tools

AWS

SQL

Job description

Capital One is seeking a Data Scientist to join the Commercial Business Analysis (CBA) and CODIA teams. You will partner with data scientists, analysts, and relationship managers to deliver data-driven value for clients and the bank.

The role covers building and validating models, modernizing data infrastructure, and supporting long-term technical integrations across multiple business projects, including risk assessment and portfolio management.