Director - Credit Risk Model Development

Role Summary

We are seeking an experienced Director - Credit Risk Model Development to lead the design, development, implementation, and enhancement of advanced credit risk models across the lending portfolio. The role will partner closely with Risk, Finance, Model Validation, Technology, and Regulatory teams to develop robust analytical solutions supporting credit decisioning, capital management, stress testing, and regulatory compliance.

Key Responsibilities

- Lead the end-to-end development and enhancement of credit risk models across wholesale and/or retail portfolios, including Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), Expected Credit Loss (ECL), IFRS 9/CECL, and Basel IRB models.

- Design statistical and machine learning models for credit risk measurement, portfolio monitoring, stress testing, capital planning, and credit strategy.

- Develop robust model methodologies, documentation, governance, and implementation frameworks in line with regulatory expectations.

- Partner with Model Validation, Model Risk Management, Internal Audit, and Regulatory teams to support model review, validation, remediation, and governance activities.

- Collaborate with business, portfolio management, finance, and technology teams to translate business requirements into analytical solutions and production-ready models.

- Perform portfolio analytics, segmentation, sensitivity analysis, and model performance monitoring to ensure ongoing model effectiveness.

- Drive automation and modernization of model development processes using advanced analytics, cloud technologies, and AI/ML where appropriate.

- Mentor and develop high-performing quantitative teams while fostering a culture of innovation, technical excellence, and continuous learning.

Preferred Experience

- 15+ years of experience in Credit Risk Analytics or Model Development within global banks, financial institutions, or consulting firms.

- Deep expertise in PD, LGD, EAD, IFRS 9, CECL, Basel IRB, stress testing, portfolio analytics, and model governance.

- Strong knowledge of statistical modelling techniques including logistic regression, survival analysis, time series, decision trees, gradient boosting, and machine learning methodologies.

- Hands-on experience with Python, SAS, R, SQL, and cloud-based analytical platforms.