Design, develop, and enhance statistical and quantitative models for non-retail credit loss forecasting (PD, LGD, EAD), IFRS 9 allowance, and stress testing frameworks
Advance methodologies to address non-retail modeling challenges, including data sparsity, low default portfolios, name concentration risk, expert judgment overlays, and portfolio heterogeneity
Build scalable tools and analytical solutions to support model development, testing, and implementation
Continuously improve model performance through rigorous validation, monitoring, and incorporation of emerging risk drivers
Ensure models meet internal Model Risk Management standards, regulatory expectations, and data governance requirements
Partner with stakeholders across Risk, Finance, Model Validation, and Technology to support model development, review, and implementation
Communicate insights, model assumptions, and limitations clearly to senior management and regulators.

Graduate degree in quantitative discipline (e.g., Financial Mathematics, Statistics, Economics)
Solid experience in credit risk modeling with exposure to wholesale/non-retail portfolios
Strong understanding of IFRS 9, stress testing, and regulatory capital frameworks (e.g., Basel)
Proficiency in Python or equivalent programming languages for data analysis and model development
Strong analytical and problem-solving skills with ability to work with complex, imperfect datasets
Excellent written and verbal communication skills, with ability to engage senior stakeholders.

Manager Advanced Analytics, Quants

Key skills