Credit Risk Modeling Expert

October 8, 2026

Hybrid

Senior

Warsaw, Poland

Description

Credit Risk Modeling Expert developing and implementing expected loss forecasting models for Commercial Real Estate, Commercial Industrial, or Small Business portfolios in compliance with CECL and CCAR. Responsibilities span model development (PD/LGD/EAD), model review and challenge, regulatory submissions, and automation with AI tools. The candidate needs a quantitative Master's or PhD, five-plus years of credit risk modeling experience, and programming skills in Python, SAS, SQL, and R.

Responsibilities

Develop and implement 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
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
Use various programming languages (Python, SAS, SQL, R) in development and data analysis, writing and executing code in local environment and cloud platforms
Provide effective challenges to existing and new models to identify potential weak points and enhance model performance
Provide support for stress testing (CCAR) submission and CECL process; document associated portfolios analysis, respective overlays for emerging risks and reasonableness analysis
Respond to questions from senior management and regulators in a timely manner
Leverage automation tools and AI to increase efficiency, reduce operational risk, and enhance usability and interpretability of results

Requirements

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. SAS, R)
Strong analytical and problem-solving skills, coupled with thoroughness and attention to detail
Effective interpersonal, verbal, and written communication skills
Experience with cloud-based tools and infrastructure (Azure or others)
Experience working with large datasets and building or validating advanced statistical models (including regression and economic factor models)
Exposure to machine learning concepts and their application in financial services
Understanding of version control systems like Git

Conditions

Working from a company location three (3) or more days per week
Clearly defined salary ranges aligned with industry benchmarks and internal equity standards
Performance-based incentives for eligible employees awarded through transparent, objective criteria
Inclusive equitable benefits focused on financial wellbeing, health and wellness
Continuous development opportunities including training, education support, and career progression pathways
Employee recognition programs that celebrate achievements and milestones

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