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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.