Quantitative Model Analyst, Wholesale Credit Risk & CECL

U.S. Bank; Brea (CA); On-site; USD 110,000 - 165,000; Full time

Benefits offered by this job

Healthcare

401(k) plan

Life insurance

Disability insurance

Parental leave

Job summary

U.S. Bank is seeking a motivated individual contributor to support its Model Development & Decision Science team within Credit Risk Administration.

The role focuses on developing, maintaining, and monitoring expected loss forecasting models for the Commercial & Industrial portfolio to support CECL, CCAR, and related risk management needs. You will partner with teammates across credit portfolio risk management, finance, model validation, and audit, delivering clear summaries, organized materials,

Qualifications

• Bachelor's or higher in a quantitative field with extensive relevant experience
• Experience supporting model development in credit risk or related domains
• Ability to communicate results clearly to technical and non-technical partners
• Familiarity with governance, validation, and regulatory processes

Responsibilities

• Assist with data preparation, analysis, and documentation for CECL/CCAR-related models.
• Support model development, testing, benchmarking, and documentation under senior guidance.
• Collaborate with risk, finance, validation, and audit teams to ensure reproducible analyses.

Skills

Model development

Data preparation

Analytical thinking

Communication

Education

Bachelor's degree

MA/MS in quantitative field

PhD in quantitative field

Tools

Python

R

SQL

SAS

Job description

U.S. Bank is seeking a motivated individual contributor to support its Model Development & Decision Science team within Credit Risk Administration.

The role focuses on developing, maintaining, and monitoring expected loss forecasting models for the Commercial & Industrial portfolio to support CECL, CCAR, and related risk management needs. You will partner with teammates across credit portfolio risk management, finance, model validation, and audit, delivering clear summaries, organized materials,