Responsibilities
• Provide independent oversight and challenge throughout the model lifecycle, including development, validation, approval and ongoing monitoring.
• Assess model materiality, criticality and alignment with organisational risk appetite.
• Review model documentation, assumptions, methodology, limitations, residual risks and compensating controls.
• Evaluate model monitoring frameworks, including drift detection, performance and stability metrics.
• Ensure compliance with regulatory expectations for AI/ML, including fairness, explainability and accountability.
• Collaborate with data scientists and model developers across departments to understand modelling intent and technical assumptions.
• Support enhancement of governance frameworks, policies and approval processes for statistical, ML and AI models.
• Contribute to AI/ML proof - of - concept (POC) initiatives to strengthen governance practices and support innovation.
• Partner with risk, compliance, IT and business teams to embed robust AI governance and Responsible AI principles across the organisation.
• Support AI/ML model governance by managing essential data assets, including maintaining metadata, documenting key datasets, and ensuring clarity of features and data inputs used in models.
• Support data management initiatives for building a robust AI/ML-ready ecosystem.

Requirements
• Minimum 7 years of relevant experience in model risk management, model governance, model validation, quantitative analytics or related areas.
• Strong knowledge of model governance frameworks, policies and regulatory expectations for statistical, ML and AI models.
• Understanding of AI/ML concepts including performance evaluation, explainability, drift and monitoring techniques.
• Ability to identify modelling weaknesses, design flaws, performance gaps and potential risks.
• Experience reviewing documentation, assumptions, model logic and validation evidence.
• Strong risk assessment, judgement, analytical and problem - solving skills.
• Excellent documentation and communication skills to support governance decisions.
• Ability to collaborate effectively with data science, engineering, business and risk stakeholders.
• Familiarity with Responsible AI principles such as fairness, transparency and robustness.
• Nice-to-haves: AI Governance Professional (AIGP) certificate or relevant qualifications.