Senior Model Risk Specialist

Overview

This position operates within the Model Risk Management Team, serving as the Second Line of Defense in the Risk Management division of the bank. The incumbent will oversee comprehensive model risk management activities, including independent validation, governance, risk assessment, and providing advisory support for traditional and AI-driven models.

Key Responsibilities

• Oversee the full lifecycle management of both new and existing models across the bank.
• Conduct independent evaluations of various risk models such as scorecards, IRB, ECL, AML, and fraud detection models.
• Evaluate model risk in AI applications, including conducting FEAT assessments and other AI-specific risk evaluations aligned with industry best practices and regulatory guidance.
• Review model documentation, verify implementations, and monitor model effectiveness through regular and annual assessment cycles.
• Ensure implementation of effective model risk controls throughout the model lifecycle.
• Develop and update model risk frameworks, policies, and standards to maintain compliance with regulatory mandates.
• Provide expert advice on model risk governance and recommended best practices.
• Lead initiatives to automate, standardize data, and harmonize processes related to model governance.
• Effectively communicate model risk insights and validation results to stakeholders and senior leadership.
• Collaborate with model owners, developers, and quantitative analysts to address findings and enhance the organizational model risk culture.

Qualifications and Skills

• Bachelor's degree in quantitative disciplines such as Statistics, Mathematics, Actuarial Science, Data Science, Economics, Finance, Engineering, or a relevant field; alternative equivalent experience in model risk management and validation in banking is acceptable.
• Advanced certifications such as CFA, FRM, or postgraduate degrees are advantageous.
• Proficient in various modeling and validation methodologies for both traditional and AI models, with strong familiarity with model/AI lifecycle controls.
• Knowledgeable about AI governance, with an understanding of local regulatory requirements including BNM and MAS guidelines and global AI regulatory frameworks.
• Capable of independently evaluating AI/ML risks including model explainability, fairness, robustness, and potential model drift.
• Excellent communication and stakeholder engagement skills, coupled with experience in driving automation and improving governance processes.