The Product Manager owns the end-to-end vision, strategy, and delivery of the organisation’s enterprise data hub, while concurrently driving the infusion of AI capabilities into organisational processes. This role is accountable for outcomes and impact across the full product lifecycle — from discovery to delivery, adoption, and iteration.

The data hub is a foundational product platform, enabling trusted data sharing, analytics, and AI use cases across multiple directorates. The Product Manager balances short-term delivery needs with long-term sustainability of data architecture, governance, and operating models.

This role requires at least 5 years of product management experience, strong analytical skills, and proven ability to lead cross-functional and cross-directorate teams. The Product Manager is expected to operate comfortably at both strategic and execution levels — shaping multi-year data and AI direction while remaining hands-on in driving delivery with engineering, data, policy, and operational teams.

Key Responsibilities:

Define and communicate a clear product vision and strategy for the organisation’s enterprise data hub, aligned with user needs and organisational goals.

Own the data hub as a product platform, balancing the needs of data producers, data consumers, analytics, and AI-enabled use cases.

Lead user research, customer interviews, and usability testing to identify core problems, unmet needs, and opportunities for data- and AI-enabled improvements.

Analyse quantitative and qualitative data to guide product decisions and measure success.

Translate product strategy into detailed requirements, user stories, and acceptance criteria.

Identify potential risks across the product lifecycle, including data, security, AI, and operational risks; implement mitigation measures and maintain the risk register.

Prioritise and maintain the product backlog to ensure delivery of high-impact data and AI capabilities.

Collaborate closely with engineering, design, project management, data owners, and policy teams across directorates.

Communicate product roadmap updates and manage expectations with senior leadership and stakeholders.

Plan and execute product launches; monitor post-launch performance and iterate based on outcomes.

Define “what success looks like” for the product, including key metrics, data requirements, and reporting/insight mechanisms to track outcomes.

Partner with data owners to ensure data quality, governance, access controls, and appropriate use of data across the product lifecycle.

Identify and validate AI opportunities (e.g. automation, decision support, summarisation, triage) through structured discovery, and translate them into clear use cases, success measures, and incremental deliveries.

Assess AI feasibility and risks, including data readiness, integration constraints, model limitations, ethical considerations, and operational support requirements.

Ensure AI-enabled features are operationally viable, including model lifecycle management, human-in-the-loop controls, monitoring of performance and risks, and integration into day-to-day workflows.

Drive adoption and change enablement for data and AI features (training, communications, stakeholder alignment), ensuring they genuinely extend workforce capacity and improve decision quality.

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