Lead - Credit Risk Modelling - Consulting

Role:

- The Company provides specialist advisory services across credit risk modelling, model validation, IFRS 9 implementation, portfolio analytics and regulatory risk management to financial institutions and corporate clients.

- Develop and review credit risk models using Excel - based rating agency frameworks, Python and statistical techniques including logistic regression, decision trees and ensemble methods.

- Build and enhance IFRS 9 expected credit loss models, including PD, LGD, EAD and CCF components across both simplified and general approaches.

- Develop roll - rate - based PD models using customer - level receivables data, portfolio - level data and ageing cohort analysis.

- Apply advanced methodologies including TTC - to - PiT conversion, Vasicek modelling, survival PDs and term PD structures.

- Conduct credit risk model validations aligned with OCC 2011 - 12 guidance and Basel requirements.

- Perform parameter - level validation including WoE, IV analysis, backtesting and out - of - time performance testing.

- Evaluate statistical, scorecard and hybrid risk models to ensure analytical integrity and regulatory compliance.

- Serve as a subject matter expert in client discussions, supporting solution design, proposal development and project delivery.

- Translate client requirements into practical modelling approaches and commercially relevant analytical outputs.

- Identify opportunities to expand client engagements through value - added risk advisory solutions.

- Support the development of the wider Advisory team's credit risk modelling and validation capability through mentoring, coaching and knowledge sharing.

- Create reusable modelling templates, validation frameworks and best - practice guidance to improve consistency and efficiency.

- Help colleagues develop expertise in statistical modelling, IFRS 9/ECL methodologies, model validation standards and emerging AI - related risk concepts.

Requirements:

- 7 - 12 yrs of hands - on experience in credit risk model development, model validation, IFRS 9/ECL modelling, credit analytics or related risk advisory functions.

- Strong proficiency in Excel and Python for statistical analysis (logistic regression, decision tree, ensemble methods), model development and validation.

- Deep understanding of credit risk methodologies, scorecard development, rating frameworks, model lifecycle management and regulatory model governance.

- Demonstrated ability to independently manage complex analytical projects and client engagements.

- Strong stakeholder management and communication skills, with the ability to explain complex technical concepts clearly.

- Experience in ECL modeling using both simplified and general approaches as per IFRS guidelines.

- Experience of TTC to PiT conversion using Vasicek models.

- Knowledge of EXCEL and Python libraries for multivariate/ statistical modeling.

- Some exposure to AI models in context of credit risk.

- Additional experience in stress testing, reverse stress testing and scenario - based portfolio analytics will be preferred.