Regional CBG (Retail) Data Chapter Lead, Transformation and Data

About the role

DBS is looking for a Regional CBG (Retail) Data Chapter Lead within its Transformation and Data function, reporting to the Group COO. The role owns the end-to-end data, analytics and AI ecosystem, combining business strategy with deep technical execution. It leads a diverse team of Data Scientists, Data Translators and Business/Data Analysts, giving them the tools, skills and strategic direction to build, operate and scale business-critical solutions.

Key responsibilities

• Drive business outcomes from data analytics: partner with CBG leadership on high-impact opportunities, develop an analytics strategy around AI/ML including Gen AI and Agentic AI, and guide prioritization of analytics workstreams and backlogs.
• Build and operate data products end to end, from build and deployment to maintenance and continuous improvement, with scalable, resilient pipelines supported by technology stakeholders.
• Promote AI industrialization and adoption of the DBS AI Protocol and reusable data science assets, and support reusable components (data, compute, models, monitoring) across the AI lifecycle.
• Shape the employee value proposition, develop talent through expertise, standards and technical direction, and lead, mentor and manage a chapter of data professionals, including performance and career progression.
• Ensure AI/GenAI models comply with internal AI governance standards and champion data quality and data governance.

Required profile

• 15+ years of experience in data science and analytics, with at least 3+ years in a senior leadership role.
• Graduate or post-graduate degree, preferably in a science or finance-related discipline.
• Proven track record of taking advanced AI products from ideation to production at enterprise scale.
• Strong business experience in financial services, preferably banking, with several years managing data analytics/AI product delivery.
• Banking domain knowledge, ideally in institutional banking or banking operations.

Required skills

• Familiarity with data analytics tools, techniques and industry trends.
• Knowledge of predictive, prescriptive, ML and AI techniques, including Generative and Agentic AI, and modern AI architectures and agentic workflows.
• Understanding of modern data stacks, cloud platforms and MLOps principles.
• Leadership, stakeholder management, communication and presentation skills, and tolerance for ambiguity.