Model Risk Management/Quantitative Risk/AI Model Governance Manager (Banking)

Model Risk Management/Quantitative Risk/AI Model Governance Manager Position Overview Working directly alongside the Team Head, this Manager will join a core risk analytics function responsible for broad credit risk models, stress testing, and BASEL frameworks. Within this scope, the role focuses specifically on second-line (2LOD) oversight, review, and constructive challenge for artificial intelligence and machine learning (AI/ML) deployments institution-wide. This hands-on position provides broad exposure to cutting-edge AI applications, helping execute the bank's Responsible Use of AI framework while supporting both traditional risk analytics and the ongoing governance of AI models. Key Responsibilities AI Use Case Evaluation & Review Risk Tiering: Conduct preliminary risk assessments for new AI deployments—evaluating impact, complexity, and dependencies to categorize them appropriately (High, Medium, Low, or Whitelisted). Primary Evaluator: Serve as the main reviewer for low-risk tools and whitelisted use cases, verifying that they satisfy minimum governance and security standards. Formal Challenge: Independently review technical documentation for medium- and high-risk AI models, confirming alignment with internal validation policies and regulatory standards. Governance Framework & Policy Maintenance Risk Taxonomy & Mapping: Apply and update the AI Model Risk Taxonomy across traditional statistical models and Generative AI. Support the Team Head in operationalizing the Residual Risk Framework by identifying control gaps relative to inherent risks. Policy Enhancement: Contribute to updates of internal AI governance policies and guidance to align with evolving regulatory requirements (e.g., HKMA, MAS) and risk limits. Monitoring, Inventory & Compliance Model Performance Tracking: Monitor periodic evaluation reports to identify AI-specific performance issues, such as data drift, model decay, and algorithmic bias. Central Inventory: Assist in building and maintaining an accurate, audit-ready AI Use Case Inventory. Regulatory Alignment: Track regional and international AI regulatory standards and conduct gap analyses to ensure continuous compliance. Stakeholder Engagement Collaborate closely with data science teams, model developers, and business stakeholders to provide guidance on model documentation, risk classification, and governance approval pathways. Candidate Requirements Experience: 3–5 years of experience in Model Risk Management (MRM), Model Governance, or Data Science within the financial services sector (ideal for a junior Manager caliber candidate). Education: Bachelor’s or Master’s degree in a quantitative field (e.g., Statistics, Data Science, Mathematics, Quantitative Finance, Risk Management, or Computer Science). Technical Knowledge: Solid understanding of statistical and machine learning methodologies (NLP, tree-based models, deep learning) and AI risk assessment criteria (Explainability/XAI, Fairness, Robustness). Technical & Tooling Skills: Strong preference for proficiency in Python or R (e.g., NumPy, Pandas); familiarity with enterprise model governance platforms is an advantage. Regulatory Understanding: Working knowledge of regional regulatory guidance (e.g., HKMA guidance on AI) and general MRM principles. Soft Skills: Sound judgment in navigating fast-evolving regulatory landscapes, strong written and verbal English communication, and high attention to detail as a hands-on contributor.