Help shape how responsible data and AI practices are embedded across a complex, fast-evolving regulatory landscape. You will play a visible, high-impact role influencing controls, evidence, and outcomes for AI, privacy, and data governance. Working across business, technology, legal, and data and analytics teams, you will turn regulatory expectations into practical oversight and clear actions. You will build deep domain expertise while helping teams deliver safer, more trusted solutions. If you like solving ambiguous problems with rigor and clarity, this role is for you.
As an APAC Data & AI Compliance and Operational Risk Management Vice President within Compliance, Conduct and Operational Risk, you provide independent oversight and credible challenge across AI, privacy, and data risk. You will lead monitoring and testing reviews end-to-end, assess risk and controls, and drive effective issue management. You will support regulatory engagement activities by translating requirements into clear obligations, control standards, and evidence expectations. You will partner closely with stakeholders to strengthen governance and embed responsible practices into delivery. You will produce decision-ready reporting that helps leaders understand risk themes, trends, and priorities.
Job responsibilities
- Provide independent oversight and credible challenge of compliance with applicable AI, privacy, and data risk requirements across Asia Pacific.
- Lead monitoring and testing reviews end-to-end, including scoping, walkthroughs, evidence collection, control testing, documentation, reporting, and remediation follow-up.
- Support regulatory engagements, including coordinating responses, drafting materials, preparing briefing packs, and tracking commitments through closure.
- Conduct risk and control assessments, evaluating control design and operating effectiveness and recommending practical enhancements.
- Review governance and controls for new and changing AI and data initiatives, challenging the completeness and quality of supporting artifacts.
- Translate evolving regulatory expectations into clear compliance obligations, control standards, testing approaches, and evidence requirements.
- Partner with business, technology, legal, and data and analytics stakeholders to align interpretations and embed expectations into delivery and control frameworks.
- Drive issue identification and remediation governance, including root-cause analysis support, action plan challenge, milestone tracking, and closure validation.
- Produce clear management reporting on review results, issues, trends, and emerging risks, escalating material concerns through appropriate governance channels.
- Contribute to internal working groups and, as needed, industry or regulatory forums through concise summaries, position notes, and technical/regulatory analyses for senior stakeholders..
- Stay ahead of emerging APAC regulatory themes in AI, privacy, and data governance; proactively assess impacts and propose targeted oversight priorities.
Required qualifications, capabilities, and skills
- Bachelor’s degree in Computer Science, Data Science, Information Systems, Data and Information Architecture, or a related field.
- 6 years of relevant experience in compliance, operational risk, technology risk, privacy, data governance, or AI risk management within financial services or another regulated industry.
- Demonstrated ability to independently deliver risk or compliance reviews end-to-end, including test approach design, evidence evaluation, conclusions, and high-quality written reporting.
- Experience supporting regulatory examinations or engagements related to privacy, data, technology risk, and/or AI.
- Ability to interpret regulatory requirements and convert them into control standards, testing approaches, and objective evidence expectations.
- Working knowledge of data governance practices and common capabilities such as data catalogs, data lineage, data quality controls, access controls, and retention.
- Working knowledge of artificial intelligence and machine learning (AI/ML), including generative AI and agentic AI concepts, key risks, and practical controls.
- Familiarity with large language model (LLM) risk concepts such as prompt patterns, evaluation approaches, and safety and guardrail practices.
- Familiarity with model explainability and fairness concepts and common approaches (for example, SHAP, LIME, or comparable techniques).
- Familiarity with privacy-enhancing techniques such as anonymization, differential privacy, and federated learning concepts.
- Strong stakeholder management and communication skills, with the ability to influence outcomes through clear, risk-based guidance without formal authority.
Preferred Qualifications, Capabilities, and Skills
- Experience operating within APAC in a second-line compliance/operational risk model.
- Hands-on experience performing thematic reviews and control testing across data/AI domains (e.g., lineage validation, access governance, bias testing protocols, explainability documentation, privacy impact assessments).
- Strong execution discipline with the ability to manage multiple priorities, stakeholders, and deadlines in parallel.
- Data analytics and visualization skills (e.g., Python, R, Alteryx, Tableau, Excel) to support monitoring, testing, and trend-based risk insights.
- Relevant certifications (e.g., CIPP/A, CIPM, CIPT, CDMP, risk/compliance qualifications) and/or formal AI governance coursework.
- Experience partnering with Model Risk, Technology Risk, Data Governance, and Cyber/Information Security teams in a complex, matrixed environment.