ECL Model Officer

Overview
We are looking for an ECL Model Officer who will develop, enhance, and monitor Expected Credit Loss (ECL) models for Consumer and SME lending portfolios. The role combines quantitative analysis, credit risk understanding, and hands-on model development to support regulatory and internal risk management requirements.

Key Responsibilities

• Develop and enhance ECL models for Consumer and SME products (e.g., PD, LGD, EAD and related model components).
• Perform data preparation, analysis, and feature engineering to support model development and refinement.
• Conduct back-testing, benchmarking, and model performance monitoring to track stability and predictive power.
• Prepare model documentation, including methodology, assumptions, limitations, and validation results.
• Support IFRS 9 / regulatory ECL exercises and stress testing-related analysis as needed.
• Work closely with Credit Risk, Portfolio Management, Finance, and IT/Data teams to implement and operationalize models.
• Identify model risks, issues, and improvement opportunities and recommend corrective actions.
• Produce regular management reports on model performance, trends, and key risk indicators.

Qualifications

• Bachelors degree in Statistics, Mathematics, Engineering, Economics, or related quantitative course.
• At least 2 years of experience in ECL model development and/or credit risk modeling (banking or financial services).
• Hands-on experience with credit risk models (e.g., PD/LGD/EAD, scorecards, ECL frameworks).
• Strong skills in data analysis and programming using tools such as SAS, R, Python, SQL, or similar.
• Solid understanding of credit risk concepts, portfolio behavior, and basic banking products (Consumer and SME).
• Strong analytical, problem-solving, and documentation skills.
• Good communication skills and the ability to explain technical concepts to non-technical stakeholders.

Preferred / Advantage

• Exposure to IFRS 9 or other regulatory ECL frameworks.
• Experience in a bank, fintech, or financial institution within Risk, Analytics, or Finance.
• Experience working with large datasets and data warehouses