Risk Manager, AI Model Governance

We are partnering with a leading financial institution to identify a proactive and detail‑oriented Risk Manager to provide second‑line oversight, review, and challenge for AI and machine learning model risk initiatives across the organization.

You will support the embedding of the Bank's Responsible AI framework, with a focus on conducting AI model risk assessments and ensuring consistent application of risk standards.

This role offers excellent exposure to a wide range of AI initiatives within a leading financial institution, with the opportunity to contribute directly to the firm's risk management evolution.

Key Responsibilities

• Conduct initial risk assessments on new AI initiatives – evaluate impact, reliance, and complexity; ensure accurate categorization within the governance pipeline
• Lead reviewer for Low‑Risk and Whitelisting processes, assessing automated tools and use cases against security and governance thresholds
• Review High/Medium risk documentation for compliance with the Bank's Validation Framework and regulatory expectations
• Maintain the AI Model Risk Taxonomy – from GenAI to traditional regressions – ensuring risks are mapped to appropriate controls
• Support development and operationalization of the Residual Risk Framework through gap analysis between inherent risks and existing controls
• Assist in updating AI Model Governance policies, standards, and guidelines to reflect evolving risk appetite and regulatory changes
• Review model performance reports for AI-specific issues including data drift, model decay, and output bias
• Contribute to building and maintaining a centralized, audit‑ready AI Use Case Inventory
• Monitor regulatory developments (e.g., HKMA, MAS) and conduct gap analyses to ensure compliance
• Partner with model developers, data scientists, and business units on model documentation, risk tiering, and approval processes

Essential Experience and Skills

• Degree in a quantitative field (Statistics, Data Science, Mathematics, Quantitative Finance, Risk Management, Computer Science, etc.)
• 3–5 years in Model Risk Management, Model Governance, or Data Science in financial services
• Understanding of statistical modelling and ML algorithms (NLP, Tree‑based models, Deep Learning) and AI evaluation standards (XAI, Fairness, Robustness)
• Proficiency in Python or R (NumPy, Pandas) – banking governance platform experience is a plus
• Knowledge of HKMA AI guidance and Model Risk Management frameworks (e.g., SR26‑2)
• Strong judgement, excellent communication skills, and high attention to detail