Quantitative Model Analyst

In this role, you will lead the development and implementation of expected loss forecasting models for Commercial Real Estate (CRE), Commercial Industrial (C&I), or Small Business portfolios, ensuring compliance with CECL, CCAR, and other regulatory requirements.

The ideal candidate is expected to have a good understanding of commercial portfolios and statistical methods, industry experience, U.S. banking regulations, excellent communication, and attention to detail as well as a strong background in predictive modeling.

U.S. Bank is a financial institution helping customers and businesses make better financial decisions and supporting communities. Elavon, a wholly owned subsidiary, provides payment solutions to over 1.5 million customers worldwide, focusing on innovation and customer service.

• Master's Degree or PhD in a quantitative field such as computer science, data science, mathematics, or statistics.
• 8 or more years of experience in credit risk modeling and industry-standard approaches (e.g. PD, LGD, EAD).
• Deep understanding of banking, financial metrics, and credit risk management.
• Knowledge of banking regulation and requirements for stress testing and credit reserves.
• Programming experience in Python (preferred) or similar statistical software (e.g. R, SAS).
• Experience with cloud-based tools and infrastructure (Azure or others).
• Strong analytical and problem-solving skills, coupled with thoroughness and attention to detail.
• Effective interpersonal, verbal, and written communication skills.
• Ability to prioritize work, meet deadlines, and work under pressure and independently while balancing multiple priorities in a dynamic and complex environment.

Nice to have:

• Experience in financial services, banking, and/or credit risk.
• Experience working with large datasets and building or validating advanced statistical models (including regression and economic factor models).
• Familiarity with automation using scripting tools (e.g. Bash) and low-code platforms (Microsoft Power Automate / Power Apps).
• Exposure to machine learning concepts and their application in financial services.
• Understanding of version control systems like Git.
• Experience in Data visualization tools such as Power BI.

Model Development:

• Develop expected loss forecasting models (PD/LGD/EAD) with best practice and document model methodology, selection evidence, model performance testing for validation and regulatory review.
• Review and revise segmentation and modeling approach based on changes in business unit, portfolio or economic intuition.
• Analyze model metrics (e.g., accuracy, stability), identify issues, and recommend improvements.
• Coding: Use various programming languages (Python, SAS, SQL, R) in development and data analysis. Write and execute code in both local environment and cloud platforms (Azure or others).
• Model Review: Provide effective challenges to existing and new models to identify the potential weak points and enhance model performance.
• CCAR/CECL Submission: Provide support for stress testing (CCAR) submission and CECL process; document associated portfolios analysis, respective overlays for emerging risks and reasonableness analysis; respond questions from senior management and regulators in a timely manner.
• Transformation: Leverage automation tools and AI to increase efficiency, reduce operational risk, and enhance usability and interpretability of results.

This role requires working from a U.S. Bank location three (3) or more days per week.