Executive Director, Data and AI Delivery

Key Responsibilities

1. Analytics Delivery & Product Execution

• Lead the collaborative design, build, and delivery of analytics and AI products that address client, market, and business needs across the APAC region
• Architect and deliver the next generation of cloud-native data products on Azure and Databricks, creating the scalable data foundations required to power AI, GenAI, and Agentic AI solutions
• Translate strategic priorities into clear analytics roadmaps, delivery plans, and prioritised use-case backlogs aligned to the CDAO's ABCD framework
• Drive analytics outputs beyond static reporting towards actionable, repeatable, and scalable data products
• Manage delivery cadence using Agile or hybrid methodologies, ensuring timely execution against milestones and business outcomes

2. Next-Generation AI & Agentic Solutions 

• Design and build the data product layer that enables the scaling of AI and Agentic AI solutions — autonomous, multi-step AI systems capable of reasoning, planning, and executing complex workflows on behalf of business users
• Leverage Azure AI Services, Databricks MLflow, and modern orchestration frameworks to develop, test, and deploy AI agents that augment decision-making across client analytics, risk, and operations
• Establish reusable data product patterns (feature stores, curated data sets, real-time pipelines) that accelerate AI model development and Agentic solution deployment
• Stay at the cutting edge of emerging AI paradigms — including multi-agent architectures, retrieval-augmented generation (RAG), and tool-using AI agents — and translate them into practical, governed, enterprise-grade solutions
• Partner with the AI Risk Management function to ensure all AI and Agentic solutions comply with model risk, explainability, and ethical AI standards

3. Client, Market & Stakeholder Engagement

• Partner with business, client-facing, and market teams to identify and prioritise high-value analytics and AI opportunities
• Co-develop analytics roadmaps for priority clients, markets, and business lines in collaboration with senior leadership
• Demonstrate the art of the possible — showcasing how next-generation data products and Agentic AI can unlock new revenue streams, improve client experience, and drive operational efficiency
• Communicate analytics insights, delivery trade-offs, and programme progress clearly and effectively to senior stakeholders, committees, and governance forums

4. Tools, Platforms & Enablement

• Operate at the forefront of SMBC's cloud data ecosystem, leveraging Azure (Data Factory, Synapse, AI Services), Databricks (Unity Catalog, Delta Lake, MLflow), Power BI, and Alteryx to build and scale enterprise-grade data and AI products
• Ensure all analytics and AI products are built on governed, quality-assured data assets, aligned with Collibra metadata standards and control frameworks
• Collaborate closely with data platform and engineering teams to deliver scalable, resilient, and production-ready solutions
• Champion the adoption of modern engineering practices — including Infrastructure-as-Code (IaC), CI/CD pipelines, and MLOps — to accelerate the delivery of AI-ready data products

5. Governance, Quality & Risk

• Apply enterprise data governance, data quality, and model risk management standards across all analytics and AI products
• Ensure analytics and AI solutions comply with internal control frameworks, data privacy regulations, and regional regulatory requirements (e.g., MAS, HKMA)
• Embed responsible AI principles into the design and deployment of all AI and Agentic solutions, including transparency, fairness, and human-in-the-loop controls
• Proactively identify, assess, and mitigate analytics delivery and operational risks, escalating where appropriate

6. Team Leadership & Continuous Improvement

• Lead, mentor, and develop a high-performing team of analytics engineers, data scientists, and delivery professionals
• Build a team culture of curiosity and innovation — encouraging experimentation with emerging technologies, hackathons, and proof-of-concept sprints
• Foster a culture of collaboration and continuous improvement across the analytics and AI development lifecycle
• Promote best practices in analytics development, data visualisation, code quality, and product adoption
• Define and track analytics product KPIs, value metrics, and adoption rates to measure effectiveness and drive continuous uplift