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Job Summary

The Data & AI Lead is responsible for identifying, shaping, and delivering AI-enabled products that improve risk and compliance processes and deliver measurable business value.
The role sits within the AI Centre of Excellence (CoE) and acts as the bridge between business, data, AI engineering, and governance teams, ensuring AI solutions are aligned to strategic priorities, deliver tangible outcomes, and are successfully adopted by business users.
In addition to product ownership, the role drives process excellence and value realisation, ensuring AI initiatives generate measurable productivity, risk reduction, and operational efficiency improvements.

Key Responsibilities

Strategy
• Define and maintain the Data & AI capability strategy and product roadmap for Risk & Compliance, ensuring AI initiatives are aligned to business priorities, enterprise AI direction and measurable value outcomes.
• Own the AI use case portfolio across assisted, enabled, generative and agentic automation, ensuring a coherent path from ideation to prioritisation, design, delivery, adoption, scale and retirement.
• Partner closely with respective Product Owners, Business Owners and Process Owners to review the full product and process journey, identifying where AI, data, automation and analytics can materially improve productivity, risk outcomes, user experience and decision quality.




• Lead structured opportunity discovery across products and processes to identify areas where process engineering through AI and data tools can deliver substantial qualitative and quantitative advantages, including cycle-time reduction, effort elimination, quality improvement, control uplift and improved decision support.
• Continuously challenge existing ways of working and push the boundaries of imagination with business stakeholders by bringing practical ideas on GenAI, agentic AI, workflow automation, data intelligence and reusable AI capability patterns.
• Support strategic buy, build, reuse or partner decisions for AI capabilities, ensuring investment is directed towards scalable, reusable and value-accretive solutions.
• Maintain a centralised AI backlog and product pipeline that balances business value, feasibility, risk, complexity, user adoption, delivery capacity and strategic relevance.

Business
• Act as the primary business-facing lead for AI capability development, building trusted relationships with Product Owners, Business Owners, SMEs and senior stakeholders to identify, shape and deliver AI-enabled business transformation.
• Work with product and business owners to assess the entire product journey, including upstream inputs, workflow steps, decision points, controls, handoffs, pain points, downstream outputs and user experience.
• Translate business challenges into clear, outcome-led AI opportunities, ensuring each use case has a defined problem statement, target user group, expected benefit,



success measure and adoption pathway.
• Lead process and value analysis to determine where AI and data tools can generate measurable business impact, including productivity gains, cost avoidance, improved quality, better risk detection, reduced manual intervention and enhanced decision-making.
• Quantify expected benefits before implementation and track realised benefits after deployment, covering both quantitative measures such as time saved, volume processed, cost avoided and cycle-time reduction, and qualitative outcomes such as improved user confidence, better insights, stronger controls and improved service experience.
• Enable product and business owners to reimagine how work can be performed using AI, including GenAI assistants, copilots, agentic workflows, intelligent triage, automated summarisation, insight generation and decision-support tools.
• Ensure AI products are business-relevant, user-centred, measurable, scalable and designed to deliver sustained adoption rather than isolated experimentation.

Processes
• Establish and operate a structured AI product lifecycle covering intake, opportunity discovery, journey review, prioritisation, requirements definition, design, development oversight, deployment readiness, adoption, value tracking and continuous improvement.
• Facilitate end-to-end product journey and process engineering reviews with Product Owners and Business Owners to identify friction, duplication, manual effort, control gaps, rework, delays and decision bottlenecks that can be improved through AI and data-enabled solutions.
• Convert business and process opportunities into well-defined product artefacts, inclu

📌 Data & AI Portfolio, Adoption & Service Excellence (Bengaluru)
🏢 Standard Chartered Bank
📍 Bengaluru