Lead Credit Risk Modeling & Forecasting (PD/LGD/EAD)

U.S. Bank; Warszawa; Hybrid; PLN 467,000 - 700,000; Full time

Job summary

U.S. Bank is seeking a Quantitative Model Analyst to lead development of expected loss forecasting models for CRE, C& I, or Small Business portfolios, ensuring CECL/CCAR compliance.

Strong programming, statistical modeling, and data analysis skills are required to support stress testing and regulatory asks. The role emphasizes collaboration with risk teams, model validation, and documentation of methodologies, with opportunities to leverage automation and AI to improve efficiency and

Qualifications

• Master's Degree or PhD in a quantitative field such as computer science, data science, mathematics, or statistics.
• 5 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.
• Optional 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.

Responsibilities

• 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 Al to increase efficiency, reduce operational risk, and enhance usability and interpretability of results.

Skills

Statistical methods

Predictive modeling

Communication

Attention to detail

Problem solving

Time management

Independent work

Interpersonal skills

Machine learning concepts

Data visualization

Education

Master's degree or PhD in a quantitative field

Tools

Python

SAS

SQL

R

Azure

Git

Power BI

Power Automate / Power Apps

Job description

U.S. Bank is seeking a Quantitative Model Analyst to lead development of expected loss forecasting models for CRE, C& I, or Small Business portfolios, ensuring CECL/CCAR compliance.

Strong programming, statistical modeling, and data analysis skills are required to support stress testing and regulatory asks. The role emphasizes collaboration with risk teams, model validation, and documentation of methodologies, with opportunities to leverage automation and AI to improve efficiency and