Executive Director, Data and AI Delivery (Singapore)

Job description

Headquartered in Tokyo, Sumitomo Mitsui Banking Corporation (SMBC) is a leading global financial institution and a core member of Sumitomo Mitsui Financial Group (SMBC Group). Built upon our rich Japanese heritage since 1876, we put our customers first and provide seamless access to, from and within the Asia Pacific region. SMBC is one of the largest Japanese banks by assets and maintain strong credit ratings across our global integrated network. We work closely as one SMBC Group to offer personal, corporate and investment banking services to meet the needs of our customers. With sustainability embedded within our strategy and operations, we are committed to creating a society in which today’s generation can enjoy economic prosperity and well-being, and pass it on to future generations. 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 Required Qualifications & Experience Bachelor's degree (or higher) in a quantitative, analytical, or technology-related discipline 15+ years of experience delivering analytics, data engineering, or AI/ML solutions — ideally within banking, financial services, or regulated industries Strong hands-on knowledge of cloud data platforms ( Azure, Databricks ), analytics tools, and modern delivery practices (Agile, DevOps, MLOps) Demonstrated exposure to AI/ML product development, GenAI, and emerging Agentic AI concepts Proven ability to engage and influence senior stakeholders across business and technology functions Excellent communication skills — able to translate complex analytics and AI concepts into clear business outcomes Preferred Master's degree or MBA in a relevant discipline Experience with Azure AI Services, Databricks MLflow, LangChain , or similar AI orchestration frameworks Familiarity with data governance frameworks (e.g., BCBS 239) and metadata management tools (e.g., Collibra) Experience building or deploying Agentic AI, multi-agent systems, or RAG-based solutions in an enterprise setting Prior experience in Japanese or Asia-Pacific banking institutions Key Competencies Strategic thinker with strong execution focus · Passionate about emerging technology and innovation · Comfortable navigating ambiguity in a matrixed, multi-cultural organisation · Results-oriented with a bias for measurable impact · Committed to governance, risk awareness, and responsible AI