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 ...