Senior Quantitative Analyst, Model Risk & ML Solutions

Capital One; Riverwoods (IL); On-site; USD 123,000 - 141,000; Full time

Job summary

Capital One is seeking a Senior Associate, Quantitative Analyst in the Model Risk Office to advance forecasting and validation efforts. This role emphasizes cross-functional collaboration, cloud-based analytics, and rigorous documentation of modeling results.

You will work with teams across the bank to strengthen risk models and support data-driven decision making. The ideal candidate brings a strong foundation in econometrics, machine learning, and large data analysis, plus the ability to

Qualifications

• Master's degree in a quantitative field or MBA with quantitative concentration by start date.
• At least 1 year of experience in statistical or econometric modeling and related techniques.
• Experience in 3 of: survival analysis, time-series, panel data, cross-sectional data, machine learning, large datasets.

Responsibilities

• Partner with the various lines of business to enhance modeling and analytical framework.
• Work across Capital One entities to create novel analytical solutions to business problems.
• Identify opportunities to apply quantitative methods and automation to improve performance and efficiency.
• Collaborate in a cross-disciplinary team to build cloud-based solutions grounded in data.
• Identify opportunities to apply quantitative methods or machine learning to improve business performance.

Skills

Quantitative analysis

Econometrics

Regression analysis

Python/R/SQL

Model risk

Education

Master's in quantitative field

MBA with quantitative concentration

Tools

Python

R

SQL

Job description

Capital One is seeking a Senior Associate, Quantitative Analyst in the Model Risk Office to advance forecasting and validation efforts. This role emphasizes cross-functional collaboration, cloud-based analytics, and rigorous documentation of modeling results.

You will work with teams across the bank to strengthen risk models and support data-driven decision making. The ideal candidate brings a strong foundation in econometrics, machine learning, and large data analysis, plus the ability to