Director, AI Risk Governance & Validation Lead (Singapore)

Job description

1. Lead the DMO AI Risk Governance & Validation Team Lead a four-member specialist team covering AI model governance, model validation, AI testing / QA / evaluations, and AI engineering for governance validation. Set the operating cadence, work allocation, review standards, escalation protocols and evidence-quality expectations for the team. Build a practical Line 1.5 AI governance capability that supports AI use case intake coordination, preliminary risk triage, evidence completeness checking, control effectiveness challenge and post-deployment governance. Ensure the team remains a technical assurance and evidence-review function, not an AI delivery owner, model owner or final risk approval owner. Develop team capabilities across AI governance, model risk, model validation, testing, data governance, GenAI, agentic AI, MLOps and regulatory expectations. 2. Operate Line 1.5 AI Governance Review and Challenge Review AI use cases from data governance, data risk, model lifecycle and practical control perspectives before escalation to independent risk and compliance stakeholders. Challenge the completeness and quality of AI review submissions, including business purpose, data sources, model / agent design, preliminary risk classification, validation evidence, control evidence and monitoring plans. Review whether AI controls are operationally workable, evidenced and sustainable, rather than policy-level only. Coordinate with Business / Use Case Owners, Data Owners, AI delivery teams, Technology / Security teams, Risk Management Department, Compliance Department, Legal Department, local data protection officers and other relevant control functions. Provide clear review conclusions, challenge points and remediation recommendations to support independent risk review and governance committee decision-making. 3. Data Governance for AI Review AI input data sources, data ownership, data classification, personal data / PII treatment, data access controls, cross-border considerations, data quality, lineage, metadata and evidence readiness. Confirm whether data used for AI use cases is appropriately approved, fit for purpose, traceable and subject to adequate controls. Challenge whether data access, storage location, output retention, SharePoint / platform access and downstream use are consistent with applicable data governance and privacy requirements. Work with Data Governance, Data Owners and control functions to establish a unified evidence layer covering data classification, data access, cross-border sharing, data protection, data quality, lineage, metadata and AI data risk assessment. Support AI-ready data governance standards, evidence templates and review checklists. 4. Model / Agent Validation Challenge Oversee review and challenge of model / agent validation evidence, including methodology, assumptions, feature logic, data inputs, limitations, performance, stability, robustness, explainability and monitoring design. Ensure validation evidence is appropriate for use case risk level, intended use and AI lifecycle stage. Challenge model / agent performance metrics, drift monitoring, bias / fairness assessment, robustness testing, output accuracy testing and human-in-the-loop controls. Work with model developers, AI engineers, data scientists and independent risk reviewers to ensure validation artefacts are clear, complete and decision-useful. Ensure validation evidence clearly identifies limitations, residual risks, control gaps and remediation actions where required. 5. AI Testing, QA and Evaluation Oversight Oversee testing approaches for statistical models, ML models, LLM applications and agentic AI systems. Ensure coverage of functional testing, regression testing, scenario-based testing, edge cases, adverse / irregular scenarios, bias / fairness evaluation, robustness analysis, drift detection and workflow reliability. Review end-to-end AI workflows, including data inputs, feature transformations, task completion, tool-use accuracy, API / connector behaviour, multi-step reasoning and output quality. Promote test logs, evaluation results, benchmarking, traceability and observability evidence to support AI risk review. Ensure testing findings are documented clearly and translated into remediation actions, risk caveats or acceptance considerations for governance forums. 6. AI Engineering and MLOps Governance Provide leadership oversight over technical review of AI engineering, agentic workflows, RAG, API integration, tool-use orchestration, Copilot Studio / Power Automate-type workflows, logging and monitoring controls. Challenge whether AI systems have appropriate MLOps / lifecycle controls, including model registry, version control, deployment controls, monitoring, retirement triggers and change management. Review whether AI / GenAI solutions disable inappropriate model training or data leakage pathways where required. Assess whether technical architecture and workflow design support auditability, explainability, resilience and responsible AI expectations. Partner with Technology, Security and AI delivery teams to embed technical controls early enough in the lifecycle. 7. Governance Framework, Procedures and Evidence Standards Translate AI governance policy, risk appetite and regulatory expectations into practical review procedures, templates, evidence packs and operating standards. Maintain AI review checklists and evidence standards covering lifecycle governance, data handling, access control, validation, testing, monitoring and control effectiveness. Ensure DMO review outputs are structured, audit-ready and reusable for independent risk review and committee escalation. Develop reporting on review pipeline, common evidence gaps, key control weaknesses, review turnaround, remediation status and recurring AI risk themes. Support continuous improvement of the AI Risk Governance operating model, including workflow tooling, intake process, inventory integration and review status tracking. 8. Senior Stakeholder Management and Regulatory Readiness Act as the senior DMO point of contact for AI risk governance matters with Risk Management Department, Compliance Department, Technology / Security teams, AI delivery teams, business use case owners and senior management. Explain complex AI, data and model-control issues in a clear, practical and decision-oriented manner for senior stakeholders. Support management discussions on AI governance, Line 1.5 operating model, staffing, capability build-out and APAC implementation roadmap. Contribute to regulator-ready documentation and evidence where AI governance, data handling, model validation or control effectiveness needs to be demonstrated. Bring external market awareness of AI governance practices in regulated financial services and adapt them to the APAC DMO operating model.