Senior Model Risk Specialist

The role sits in the Model Risk Management Team. This is a Second Line of Defense role in the Risk Management function. The role is responsible for managing the model risk management activities across the Bank through independent model validation, governance, risk assessment, and advisory support covering traditional and AI models.

Job Responsibilities:

• Manage the end-to-end model lifecycle for new and existing models across the Bank.

• Perform independent validation of risk models, including scorecards, IRB, ECL, AML, Fraud and other models.

• Assess model risk for AI use cases, including FEAT assessments and/or AI-specific risk assessment, in line with industry best practices or regulatory guidelines.

• Review model documentation, test model implementation, and assess model performance through ongoing and annual reviews.

• Ensure model risk controls are embedded across the model lifecycle.

• Maintain and enhance model risk frameworks, policies, and standards in line with regulatory requirements.

• Provide advisory support on model risk governance and best practices.

• Drive automation, process harmonisation, and data standardisation in model governance.

• Communicate model risk findings and validation outcomes to stakeholders and senior management.

• Work with model owners, developers, and quantitative analysts to remediate findings and strengthen model risk culture.

Requirements:

We're looking for dynamic individuals with interest in financial markets. Applicants should possess the following:

• Degree in a quantitative field such as Statistics, Mathematics, Actuarial Science, Data Science, Economics, Finance, Engineering, or a related field; or with relevant experience in model risk management, validation, or model governance in banking. Postgraduate, CFA and/or FRM qualifications are an advantage.

• Strong knowledge of modelling and validation techniques for both traditional and AI models. Familiar with model/AI lifecycle controls.

• Familiarity with AI Governance landscape and have good understanding of relevant regulatory and industry guidance, including BNM and MAS expectations on emerging AI governance standards.

• Ability to independently assess AI/ML risks such as explainability, fairness, robustness, and model drift.

• Strong communication, stakeholder management, and collaboration skills, with exposure to automation and governance process improvement.