SVP, AI Risk: Data and AI Risk

About the role

The Business Function Risk Management Group (RMG) develops and maintains risk management and internal control frameworks and provides independent review of risk/return decisions. This SVP role sits within the Data & AI Risk (DAR) team and focuses on developing and refining the Group's AI risk framework so that it is practical, aligned to regulatory expectations and applied consistently across the organisation. The position is based in DBS Asia Central.

Key responsibilities

• Lead the development and continuous evolution of the Group's AI risk framework (policy, taxonomy and control expectations).
• Ensure the framework reflects real use cases, emerging risks and regulatory direction, and define risk guardrails aligned to Group risk appetite.
• Maintain a clear view of AI use cases, platforms and developments across the business.
• Engage with business and technology teams so that risk considerations are incorporated early, and translate policy into practical, implementable requirements.
• Work with standard and control owners to align implementation with risk expectations and ensure consistent interpretation across the Group.
• Provide pragmatic risk views on complex or ambiguous AI risk matters and support the articulation and resolution of control gaps.
• Keep abreast of industry developments and regulatory trends in AI risk and turn them into updates to internal frameworks and guidance.
• Contribute to building a cohesive and credible AI risk capability within the team.

Required profile

• 8+ years of experience in risk management, with grounding in at least one established discipline (e.g. model risk, cyber risk, data risk) and experience applying it to AI or advanced analytics use cases.
• Experience developing and implementing risk frameworks or policies with a focus on real-world application.
• Good understanding of AI-related risks or adjacent domains; experience in AI risk, regulated environments or advisory roles is advantageous.
• Bachelor's degree in a relevant field (e.g. Accounting, Banking, Computer Science, Engineering, Mathematics, Statistics); relevant certifications such as CRISC or CISM are advantageous.

Required skills

• Strong knowledge of regulations and industry practices in Data and AI risk, including model risk management frameworks.
• Strong judgement in ambiguous, evolving areas, and strong analytical and problem-solving skills.
• Excellent communication skills with technical and non-technical stakeholders.