Senior Quantitative Model Validator - Risk & Regulation

Us Bank; Charlotte (NC); On-site; USD 98,000 - 116,000; Full time

Benefits offered by this job

Healthcare (medical, dental, vision)

Retirement plan 401(k)

Paid vacation

Disability insurance

Parental leave

Job summary

U.S. Bank is seeking a senior model validator to independently validate market risk and capital markets models across the lifecycle, including VaR, counterparty risk, and xVA analytics.

The role requires rigorous testing, documentation, and collaboration with risk managers and governance teams. Ideal candidate has advanced degrees in quantitative fields and strong Python skills, with experience in regulatory expectations (SR 26-2, OCC equivalents).

Qualifications

• Bachelor’s degree in a quantitative field and 5+ years of relevant experience; OR MA/MS and 3+ years; OR PhD with <2 years.
• Strong knowledge of market risk, capital markets concepts, derivatives, pricing, and risk measurement.
• Experience with statistical and quantitative modeling techniques (regression, time series, simulation).

Responsibilities

• Perform independent validation of market risk and capital markets models (VaR, counterparty risk, xVA, stress tests).
• Execute validation across model lifecycle: conceptual soundness, quantitative testing, implementation controls, monitoring.
• Prepare validation documentation aligned with internal standards and regulatory expectations; deliver reports and presentations.

Skills

Market risk concepts

Quant modeling techniques

Python or similar tools

Regulatory expectations

Communication and leadership

Education

Bachelor’s degree in quantitative field

MA/MS in quantitative field

PhD in quantitative field

Tools

Python

Job description

U.S. Bank is seeking a senior model validator to independently validate market risk and capital markets models across the lifecycle, including VaR, counterparty risk, and xVA analytics.

The role requires rigorous testing, documentation, and collaboration with risk managers and governance teams. Ideal candidate has advanced degrees in quantitative fields and strong Python skills, with experience in regulatory expectations (SR 26-2, OCC equivalents).