Director, Data, Analytics & AI - FR/ERM (Bengaluru)

#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply-#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply- Requisition Number: 54409 Job Location: Bangalore, IND Global Grade: Band 5 Work Type: Office Working Employment Type: Permanent Posting Start Date: 01/07/2026 Posting End Date: 21/08/2026 : Job Summary The Director, Advanced Analytics & AI is a techno-functional leader responsible for designing, building, and industrialising advanced analytics and machine learning solutions that enhance the banks financial risk management, regulatory compliance, and decision-making capabilities. The role sits at the intersection of: Business (Risk / Compliance) CDO (data products) Technology (engineering & product ionisation) and ensures an end-to-end lifecycle from use case discovery to production-grade deployment, aligned to regulatory and model governance expectations Key Responsibilities Strategy Lead identification, prioritisation, and shaping of high-impact analytics & ML use cases across Financial Risk and Compliance domains Translate regulatory and business requirements into analytical problem statements and solution blueprints Own business value realisation (efficiency, risk reduction, control effectiveness, insights) Aligns with CoE mandate to drive value-led, outcome-focused AI delivery. Business Define end-to-end solution architecture for analytics and ML use cases: Feature engineering, model selection, evaluation strategy Data sourcing and transformation requirements Establish and enforce design patterns, reusable components, and modelling standards Reflects role of lead architect + capability owner in CoE model Processes Personally lead or closely supervise the development of POCs and prototypes for: New analytical patterns Complex or regulatory-sensitive use cases Validate: feasibility performance explainability Core expectation: prototype validate scale recommendation Establish reusable: feature engineering pipelines model templates evaluation frameworks Drive scaling from POCs to enterprise-grade solutions Critical to avoid one-off analytics and move to repeatable AI products Define requirements for AI-ready data products with CDO teams: curated datasets feature stores data quality & lineage Ensure alignment between: data supply (CDO) analytics consumption (AI CoE) Aligns with CoE positioning as bridge between data and intelligence People & Talent Lead through example and demonstrate the banks culture and values Key Responsibilities Risk Management Work with Technology and Data Engineering teams to industrialise solutions into production Provide oversight for: model integration pipelines, APIs, and deployment frameworks Ensure: functional correctness alignment to business intent Consistent with model: Risk/AI CoE owns logic, validation Technology owns runtime & engineering Embed analytics into: credit risk models stress testing & forecasting financial crime detection regulatory reporting analytics Ensure outputs are: explainable auditable regulator-ready Governance Define and enforce end-to-end model lifecycle controls: model documentation and explainability validation frameworks monitoring (drift, bias, performance) Ensure compliance with: Model Risk Management AI governance, fairness, explainability Regulatory expectations on AI usage Strong emphasis on governed lifecycle and audit-readiness Reporting & Stakeholder Communication Act as primary interface between business stakeholders, CDO, and Technology Engage senior stakeholders to: align priorities drive adoption manage regulatory expectations Role explicitly requires strong business-tech bridging capability Team Leadership & Capability Building Lead multidisciplinary teams of: data scientists ML engineers analytics specialists Coach teams on: model development best practices regulatory constraints production readiness Build reusable: frameworks accelerators experimentation standards Key Stakeholders Data & Analytics GenAI Specialists AI Operations Business Units Technology (AI Engineering Lead; Data Engineering Lead; platform owners) Functions CDO stakeholders (standards, platform, data foundations) AI Services Legal, Privacy, Cyber Security, Model Risk, Operational Risk Internal Audit / Assurance partners COO / Finance partners (capacity and investment planning) AI Solutions Team Compliance & Governance Skills and Experience Technical and Operational Skills Strong Hands-On Experience In: Machine Learning (Classification, Regression .