Specialist - Credit Risk Modeling

Job Description: Credit Risk Modeling Specialist

Position Overview:

We are seeking an experienced Credit Risk Modeling Specialist with hands-on expertise in developing, validating, and maintaining credit risk models (PD, LGD, EAD) under IFRS9 and Basel III frameworks. The ideal candidate will have a strong background in SAS/SQL programming, risk data ETL pipelines, statistical modeling, and regulatory reporting. The role requires collaboration with cross-functional teams to ensure models meet both business and regulatory expectations.

Key Responsibilities:

- Design, develop, and validate credit risk models including PD, LGD, and EAD for retail and wholesale portfolios.

- Conduct model performance monitoring, back-testing, benchmarking, and scenario analysis to ensure accuracy and robustness.

- Ensure regulatory compliance with IFRS9, Basel III, and other applicable standards in model development and validation.

- Build, optimize, and maintain ETL pipelines to support risk data aggregation and model execution.

- Perform delinquency, roll rate, vintage, and survival analysis to generate actionable insights.

- Collaborate with credit risk, compliance, audit, and business teams to translate requirements into modeling solutions.

- Develop data quality frameworks, audit trails, and governance mechanisms to ensure data integrity and transparency.

- Prepare technical documentation and support internal/external audits with evidence of model governance.

Required Skills & Qualifications:

- 5-10 years of experience in Credit Risk Modeling and Validation (PD, LGD, EAD).

- Strong expertise in SAS (EG/DI), SQL, and risk data management.

- Hands-on experience with IFRS9, Basel III, and ECL provisioning models.

- Proficiency in statistical modeling techniques: logistic regression, linear regression, survival analysis, time series, machine learning methods (random forest, gradient boosting).

- Experience with ETL pipeline development, data quality checks, and governance frameworks.

- Good understanding of delinquency analysis, roll rate, and staging under IFRS9 (SICR triggers).

- Experience in visualization tools (e.g., Tableau, Power BI) for portfolio monitoring and reporting.

Preferred Skills:

- Exposure to Python/R for advanced analytics and automation.

- Experience with cloud platforms (GCP, AWS, or Azure).

- Prior experience in credit risk roles with banks, consulting firms, or analytics service providers.

Education:

Bachelor's or Master's degree in Statistics, Economics, Data Science, Computer Science, Finance, or related field.