Applied Advanced Analytics Products & Use Cases

• Lead the design, development, and delivery of advanced analytics products and use cases across client, market, risk, and operational domains

• Translate business requirements into scalable analytics solutions, ensuring products move from proof-of-concept to production-grade deployment

• Partner with the Analytics Engagement Advisory Office to prioritise use cases based on strategic value, feasibility, and business impact

• Drive innovation in analytics methodologies, including predictive modelling, machine learning, NLP, and statistical analysis

• Establish product ownership disciplines, ensuring clear accountability for product performance, adoption, and continuous improvement

AI / GenAI, Data Product Engineering

• Lead the engineering and development of AI, generative AI, and data products, leveraging modern platforms including Azure, Databricks, and cloud-native architectures

• Build and operationalise GenAI capabilities including LLM-powered applications, copilots, intelligent document processing, and AI-assisted decision tools

• Establish robust data product engineering practices, including data pipelines, feature stores, and reusable data assets that underpin analytics and AI products

• Ensure AI/GenAI solutions are designed with responsible AI principles, including explainability, fairness, and human-in-the-loop safeguards

• Collaborate with the AI Risk Management function to ensure all AI products meet governance, validation, and compliance requirements prior to deployment

Model Lifecycle & MLOps & Guardrails

• Establish and operate an enterprise-grade MLOps framework for the end-to-end model lifecycle — from development, training, testing, deployment, monitoring, to retirement

• Implement automated CI/CD pipelines for model deployment, ensuring rapid, reliable, and repeatable model releases to production

• Define and enforce model guardrails, including performance thresholds, drift detection, bias monitoring, and automated alerting for model degradation

• Maintain a comprehensive model inventory, ensuring full traceability, version control, and lineage for all deployed models

• Partner with AI Risk Management to ensure models meet validation, documentation, and regulatory requirements throughout their lifecycle

Analytics, Reporting & Insights

• Lead the design and delivery of enterprise analytics, reporting, and business intelligence capabilities across APAC

• Develop scalable, self-service reporting and dashboarding solutions using platforms such as Power BI, Tableau, and Databricks SQL, enabling data-driven decision-making across the franchise

• Deliver actionable insights to senior leadership, business lines, and risk functions through structured analytics products, ad-hoc analysis, and data storytelling

• Establish data visualisation standards and best practices, ensuring consistency, accessibility, and quality across all reporting outputs

• Drive the evolution from traditional reporting to predictive and prescriptive analytics, embedding forward-looking intelligence into business processes

Orchestration & Connectivity – API / Channels / Networks

• Design and manage the orchestration layer that connects analytics and AI products to downstream business systems, channels, and client-facing platforms

• Build and maintainAPI frameworks and integration services that enable seamless, real-time delivery of analytics outputs to internal and external consumers

• Establish connectivity with enterprise data platforms, trading systems, CRM, risk engines, and digital channels to embed analytics at the point of decision

• Ensure all orchestration and API services are secure, resilient, performant, and aligned to enterprise architecture standards

• Partner with technology, digital, and operations teams to enable analytics-driven automation, straight-through processing, and intelligent workflows

Governance, Quality & Operational Excellence

• Ensure all analytics products and deliverables meet SMBC’s data governance, quality, and control standards

• Embed DevOps and DataOps best practices across the office, driving operational efficiency, reliability, and continuous improvement

• Establish and monitordelivery KPIs, including time-to-value, product adoption, model performance, and operational uptime

• Partner with data governance, risk, and compliance teams to ensure analytics outputs are accurate, auditable, and compliant with regulatory requirements

People, Capability & Performance

• Lead, develop, and mentor teams of data and advanced analytics professionals across the five sub-functions, building a high-performing, innovative, and delivery-focused capability

• Foster a culture of engineering excellence, intellectual curiosity, collaboration, and continuous learning

• Attract and retain top talent across data science, AI/ML engineering, data engineering, analytics, and platform engineering disciplines

• Establish clear career pathways and development frameworks to grow specialist and leadership capabilities within the team

Required Qualifications & Experience

• Bachelor’s degree in a quantitative, technical, or analytical discipline (e.g., Computer Science, Data Science, Statistics, Mathematics, Engineering)

• 15+ years of experience in data analytics, data science, AI/ML engineering, or technology delivery within large, complex financial institutions or technology companies

• Proven track record of leading end-to-end analytics delivery — from ideation and development through to production deployment and operationalisation at scale

• Deep hands-on understanding of modern analytics and AI platforms, including Azure, Databricks, Power BI, Python, and cloud-native architectures

• Strong knowledge of MLOps, CI/CD, model lifecycle management, and production-grade analytics engineering practices

• Exceptional stakeholder engagement and communication skills at senior leadership level

• Experience managing multi-disciplinary teams spanning data science, engineering, analytics, and platform functions