Role Summary:
We are seeking an experienced Programme Manager to lead the strategic delivery of the Data and AI Risk Programme. This is a senior-level role responsible for identifying, assessing, and managing risks associated with enterprise data and the use of artificial intelligence across business functions. The role will focus on building governance frameworks, ensuring compliance with evolving global AI regulations, and promoting ethical and secure AI practices.
This individual will act as a critical bridge between data science teams, compliance, legal, and business units to ensure responsible innovation.
Key Responsibilities:
* Lead and govern the execution of the Data and AI Risk Programme, ensuring alignment with enterprise strategy, compliance mandates, and industry best practices.
* Define the programme roadmap, risk frameworks, operating model, and success metrics.
* Collaborate with cross-functional teams—Legal, Compliance, Data Science, IT Security, Regulatory Affairs—to embed AI and data risk mitigation into enterprise processes.
* Oversee the assessment and governance of AI/ML models, ensuring transparency, fairness, explainability, and compliance with standards (e.g., EU AI Act, GDPR, ISO/IEC 23894, NIST AI RMF).
* Develop internal policies and frameworks for AI model lifecycle management, ethical review, and risk controls.
* Serve as the primary point of contact for regulatory discussions related to data and AI risks.
* Provide regular reporting to executive leadership, risk committees, and relevant external bodies.
* Lead change management, training, and internal awareness related to AI risk and governance.
* Manage programme budgets, resources, timelines, vendors, and workstream owners.
Must-Have Skills & Experience:
* 15+ years of experience in programme or project management, with significant exposure to data governance, risk management, and AI-related initiatives.
* Demonstrated experience managing complex, enterprise-wide risk or transformation programmes.
* Deep understanding of AI/ML technologies and associated risks (bias, data leakage, explainability, regulatory compliance).