
Lead Risk Modelling Expert
4 days ago
This challenging role involves developing, validating, and enhancing statistical models that support risk assessments and financial forecasts.
About the RoleWe seek an experienced Quantitative Modelling Specialist with a solid foundation in credit risk modelling, model development, and data analysis within a financial environment.
Key Responsibilities- Model Development & Enhancement: Design, implement, and refine econometric models to support credit rating methodologies.
- Build models for key risk parameters: Probability of Default (PD), Loss Given Default (LGD), and Expected Loss (EL).
- Contribute to macroeconomic scenario analysis and stress testing frameworks.
- Model Validation & Monitoring: Conduct performance testing, benchmarking, and sensitivity analysis of existing models.
- Implement validation protocols: Ensure models meet internal standards and regulatory expectations.
- Recommend enhancements based on validation outcomes and business feedback.
- Data Analysis & Management: Work with large datasets from multiple sources to support model calibration and back-testing.
- Perform exploratory data analysis and statistical testing: Inform model assumptions.
- Documentation & Communication: Prepare comprehensive model documentation for internal committees, model governance teams, and regulators.
- Communicate complex quantitative findings: Clearly explain to non-technical stakeholders.
- Candidate Profile: Bachelor's Degree in Finance / Statistics / Mathematics / Data Science / Economics, Chartered Accountant or related field.
- Master's Degree in Finance / Statistics / Mathematics / Data Science / Economics, or related field (Preferred)
- 3–5 years of experience in credit risk modelling, model validation, or quantitative analysis.
- Strong command of programming tools such as Python, R, SAS, and SQL.
- Practical understanding of credit risk models, financial instruments, and portfolio risk dynamics.
- Familiarity with regulatory frameworks like Basel III/IV.
- Excellent quantitative and analytical skills with strong attention to detail.
- Ability to manage multiple projects with minimal supervision.
- Exposure to machine learning techniques applied to credit risk or financial modelling.
- Experience with data visualization tools (e.g., Power BI, Tableau).
- Working knowledge of rating agency methodologies and capital markets.
The ideal candidate will possess excellent communication and project management skills, along with a strong background in credit risk modelling.
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