Role Purpose:

The Head of Data & AI will be the enterprise leader responsible for defining and executing the firm's Data & AI strategy, architecture, platforms, governance and capabilities.

The role will establish Data & AI as a strategic enterprise capability for the firm and its cooperative banking ecosystem, covering the full lifecycle from data acquisition and engineering to data products, analytics, machine learning, Generative AI and Agentic AI.

The incumbent will build the target-state Data & AI architecture and operating model, establish reusable shared platforms and standards, and enable high-value use cases across credit, fraud, risk, operations, customer experience, financial inclusion and rural intelligence.

The role will also own the enterprise framework for responsible, secure, explainable, auditable and production-grade AI, ensuring that AI adoption is aligned with regulatory, security, privacy and risk requirements.

Primary Job Responsibilities:

1. Enterprise Data & AI Strategy:

- Define and own the firm's multi-year Enterprise Data & AI Strategy, aligned with the organisations product, technology and business strategy.

- Establish the target-state architecture and roadmap covering data platforms, analytics, AI/ML, Generative AI and Agentic AI.

- Identify and prioritise strategic Data & AI capabilities and use cases based on business value, feasibility, risk and ecosystem impact.

- Establish Data & AI as a reusable shared capability across firm platforms, products and participating cooperative institutions.

- Define the Data & AI operating model, including organisation structure, capabilities, governance, engineering standards and partner ecosystem.

2. Enterprise Data Platform & Architecture:

- Own the architecture and evolution of the firm's enterprise data platform / lakehouse and associated data services.

- Establish standards for batch and real-time data ingestion, data modelling, data integration, APIs, data contracts, metadata, lineage and interoperability.

- Establish enterprise data quality, observability, availability, performance and reliability standards.

- Design scalable and secure data architecture supporting multi-tenant and ecosystem-wide use cases.

- Drive development of reusable data products, canonical data models and governed data services.

- Ensure appropriate data lifecycle management, retention, archival and deletion mechanisms.

3. Data Governance & Data Management:

- Establish and institutionalise the enterprise Data Governance Framework, including ownership, stewardship, classification, quality, lineage, metadata and access controls.

- Define data standards and canonical definitions for critical enterprise and ecosystem data.

- Establish mechanisms for data provenance, traceability and auditability.

- Ensure compliance with applicable data protection, privacy, localisation, cybersecurity and regulatory requirements.

- Establish data access and usage controls based on purpose, sensitivity and regulatory requirements.

- Build mechanisms to continuously measure and improve enterprise data quality.

4. AI, Machine Learning & Agentic AI:

- Define the firm's enterprise AI strategy covering traditional ML, Generative AI and Agentic AI.

- Establish reusable AI capabilities, platforms, model services, evaluation frameworks and deployment patterns.

- Drive identification and implementation of high-value AI use cases across credit, fraud, collections, risk, customer service, operations, financial inclusion and rural intelligence.

- Establish standards for model development, evaluation, deployment, monitoring, retraining and retirement.

- Establish AI evaluation frameworks covering accuracy, robustness, hallucination, bias, safety, explainability and business performance.

- Enable responsible adoption of Agentic AI with appropriate human oversight, bounded autonomy, permissions, controls and auditability.

- Ensure production AI systems are observable, measurable and continuously monitored for performance and drift.

5. AI & Model Governance:

- Establish the enterprise AI and model governance framework covering the complete model lifecycle.

- Define requirements for model documentation, validation, explainability, monitoring, change management and auditability.

- Establish independent model validation mechanisms for material or high-risk models.

- Ensure appropriate remediation where model performance, drift, bias, false positives or other risk indicators fall outside defined tolerances.

- Establish model inventory, model risk classification and lifecycle controls.

- Maintain evidence and audit trails required for regulatory, supervisory and internal governance purposes.

6. AI Trust, Security & Responsible AI:

- Establish an enterprise framework for trusted and responsible AI covering explainability, transparency, traceability, human oversight, fairness, security and accountability.

- Ensure AI systems maintain appropriate data provenance and attribution across models, agents and downstream decisions.

- Define controls for sensitive data, confidential information, model access, prompt/data security and AI-specific threats.

- Establish appropriate controls for AI-generated decisions and recommendations in regulated or financially material use cases.

- Ensure that AI systems are designed for auditability and regulatory defensibility from inception rather than retrospectively.

7. Fraud, Risk & Financial Intelligence:

- Own enterprise-level data and AI capabilities supporting fraud risk management and transaction monitoring.

- Establish scalable fraud analytics and detection capabilities, including rules, statistical models and ML-based detection.

- Ensure effective threshold calibration, false-positive management, detection-rate monitoring and model performance management.

- Partner with Risk, Compliance and business leadership to develop data-driven capabilities for credit risk, portfolio monitoring, collections and financial crime risk.

8. Data & AI Products and Business Outcomes:

- Partner with Product, Business, Risk, Compliance and Technology leadership to translate institutional priorities into a prioritised portfolio of Data & AI products and use cases.

- Define measurable business outcomes and success metrics for Data & AI investments.

- Ensure that Data & AI capabilities are designed as reusable products rather than isolated analytical solutions.

- Establish product management discipline across enterprise data products, AI services and analytical capabilities.

- Continuously evaluate adoption, value realisation and return on investment from Data & AI initiatives.

9. Ecosystem & Interoperability:

- Define standards for integrating the firm's Data & AI capabilities with core banking systems, external platforms, ecosystem partners and Indias digital public infrastructure.

- Establish data exchange, API, event and interoperability standards for participating institutions and technology partners.

- Ensure data and AI platforms can operate across heterogeneous technology environments while maintaining security, governance and data isolation.

- Drive adoption of common standards and reusable components across the firm's ecosystem.

10. Leadership & Capability Building:

- Build and lead a high-performing Data & AI organisation spanning: 1. Data Engineering, 2. Data Platform Engineering, 3. Data Architecture, 4. Analytics, 5. Data Science, 6. Machine Learning / AI Engineering, 7. AI Product Management, 8. Data & AI Governance.

- Define technical standards, engineering practices and career frameworks for the function.

- Develop internal capabilities while establishing an effective ecosystem of technology and implementation partners.

- Attract, mentor and retain high-quality Data & AI talent.

- Establish a culture of engineering excellence, experimentation, measurable outcomes and responsible innovation.

11. Vendor & Delivery Governance:

- Own governance of strategic Data & AI technology partners, systems integrators and specialist vendors.

- Hold partners accountable for committed scope, architecture, security, quality, service levels, delivery milestones and business outcomes.

- Establish structured governance mechanisms covering delivery tracking, architecture compliance, risk management and escalation.

- Ensure that the firm retains appropriate ownership and control of critical data, models, specifications, reusable components and intellectual property.

12. Regulatory, Audit & Board Engagement:

- Act as the senior enterprise point of accountability for Data & AI matters with regulators, supervisory bodies, auditors and internal governance forums.

- Lead responses to relevant regulatory inspections, thematic reviews, audits and information requests.

- Provide the Board and senior management with clear reporting on Data & AI strategy, risks, investments, outcomes and emerging technology risks.

- Ensure that material Data & AI initiatives have appropriate documentation, controls, approvals and audit evidence.

Professional Skills & Experience:

- 12- 15+ years of experience in Data, Analytics, AI/ML or technology leadership, with significant experience leading enterprise-scale Data & AI functions.

- Proven experience building a Data & AI organisation, platform or capability from the ground up.

- Strong experience in banking, financial services, fintech, payments, insurance or another highly regulated environment.

- Deep understanding of modern data architecture, including data lakehouse / warehouse architectures, batch and streaming data, data modelling, data integration, data quality, metadata and lineage.

- Strong understanding of the complete ML/AI lifecycle covering development, evaluation, validation, deployment, monitoring, drift management and retirement.

- Hands-on understanding of Generative AI and emerging Agentic AI architectures and their enterprise deployment considerations.

- Experience building or governing enterprise AI platforms and production-grade AI/ML systems.

- Strong understanding of data governance, privacy, security, localisation, access control and regulatory requirements applicable to financial institutions.

- Experience establishing responsible AI / model governance frameworks, including explainability, auditability and independent validation.

- Experience with fraud analytics, transaction monitoring, risk analytics or other financial intelligence capabilities is strongly preferred.

- Demonstrated ability to convert business priorities into a portfolio of scalable Data & AI products with measurable outcomes.

- Strong experience managing large systems integrators, technology vendors and strategic partners.

- Experience operating at senior leadership / executive level and influencing Product, Technology, Business, Risk, Compliance and Operations stakeholders.

- Ability to communicate complex Data & AI concepts clearly to Board, senior management, regulators and non-technical stakeholders.

- Strong strategic thinking combined with the ability to drive execution in a greenfield, high-growth environment.

Preferred Technical Exposure:

- Candidates should have strong familiarity with several of the following: Modern data lakehouse / data platform architectures, Cloud and hybrid-cloud data platforms, Streaming and event-driven data architectures, Data APIs and data products, Data governance, metadata and lineage platforms, MDM and enterprise data quality, Machine Learning platforms and MLOps, Generative AI and LLM architectures, Agentic AI and multi-agent orchestration, Model evaluation and observability, AI governance and responsible AI, Fraud and risk analytics, Data security, privacy and access governance, API-first and microservices architectures, Kubernetes / containerised environments, Enterprise analytics and BI platforms.

Educational Qualifications:

- B.Tech / B.E. / MCA / Masters degree in Computer Science, Data Science, Statistics, Artificial Intelligence, Engineering or an equivalent discipline.

- A postgraduate qualification in management, technology or a related discipline is desirable.

- Relevant certifications in Data Management, Cloud, AI/ML, Data Governance or Information Security are desirable.

Leadership Attributes:

- Enterprise mindset: Thinks beyond individual products and builds reusable organisational capabilities.

- Builder mindset: Comfortable creating platforms, teams, standards and operating models from the ground up.

- Technology depth: Able to challenge architecture and technology decisions at a senior technical level.

- Business orientation: Connects Data & AI investments directly to measurable institutional outcomes.

- Regulatory maturity: Understands the implications of deploying AI and data platforms in regulated financial services.

- Execution orientation: Converts strategy into roadmaps, products and measurable delivery.

- Ecosystem thinking: Comfortable operating across government, financial institutions, technology partners and digital public infrastructure.

- Responsible innovation: Balances speed of AI adoption with security, governance, explainability and trust.