About Data & AI Product Team
The Data & AI Product team is responsible for developing and managing data and AI/ML products that drive intelligent experiences, decisions, and actions across the Tesco ecosystem. We work in close partnership with business and technology teams across Customer, Finance, Supply Chain, Commercial etc.—to enable the seamless build, consumption, integration and expansion of Data & AI capabilities.
The Role - Product Manager - Data & AI
Whilst specific responsibilities will be dependent upon the changing needs of the Tesco business, the following provides an overview of the role’s key responsibilities.
As a Product Manager - Data & AI, you will be responsible for a set of complex and company strategic products with the goal of driving optimisation and efficiency using Data Science across the organisation.
Responsibilities include:
Within this key role you are accountable for the following:
Define Product Vision & Strategy - Working in close collaboration with the Lead PM, drive the vision, strategy, and long-term roadmap for your product domain.
Deliver Measurable Value - Champion a value-first mindset. Set and track OKRs and KPIs across the product portfolio with a strong focus on delivering tangible business value.
User-Centricity - Develop a deep understanding of user personas, customer journeys, and stakeholder needs to identify pain points and opportunities. Use this insight to shape product strategy, prioritise features, and ensure solutions deliver meaningful value.
Fail-fast - Champion a fail-fast mindset by encouraging rapid hypothesis-based experimentation, learning from early failures, and iterating quickly to deliver value. Promote a culture where teams feel empowered to test bold ideas, validate assumptions, and pivot when needed—ensuring continuous improvement and innovation.
Competitive Landscape Understanding - Engage with and actively involved in market research, leveraging insights to shape the vision and strategic direction
Drive Product Execution - Translate strategy into actionable plans by managing backlogs - from Opportunity backlog to Idea Backlog to Product Backlog to Sprint backlog, and steer sprint/release planning with cross-functional team / squad.
Collaborate & Influence Stakeholders - Partner with peers, senior leaders, and cross-functional teams to steer product direction and drive alignment.
Resolve Trade-offs & Live Issues - Make decisive prioritisation calls, manage conflicting demands, and independently coordinate resolution of live product issues.
Key Skills & Experience
You’ll need to have demonstrated experience of:
Proven track record in leading the end-to-end product lifecycle for Data and AI/ML products, from ideation to delivery, with measurable success across key business metrics, with minimum 2 years experience working with data science, data engineering and UI/UX teams.
Strong grasp of emerging general Data Science and AI technologies, opportunities & challenges deploying and realising value at enterprise scale
Understanding of Retail including the key commercial drivers, business metrics and performance indicators
Experienced in applying agile methodologies, setting and tracking OKRs, and using data to drive prioritisation and roadmap decisions.
Demonstrated experience applying structured product design methodologies—such as the Triple Diamond framework—to guide discovery, definition, and delivery
Experience appraising and analysing the results of user research and use the results to influence product outcomes and strategic goals.
Experienced using data to prioritise and adopting a consistent approach to prioritisation
Experienced in relevant tools such as Aha!, Jira, and Confluence, to produce and update roadmaps and using it as a tool to create buy-in from stakeholders and other teams
Comfortable navigating ambiguity, initiating market research, and using both qualitative and quantitative insights to shape product strategy.
Experienced in applying agile methodologies, setting and tracking OKRs, and using data to drive prioritisation and roadmap decisions.
Strong stakeholder management and decision-making capabilities,
Adept at balancing technical debt and innovation,
Excellent communicator with the ability to influence and present to senior leadership
Knowledge & demonstrated understanding of how large technology systems should interact with each other and track health metrics (uptime, SLOs, response time, etc)
Knowledge & demonstrated understanding of large-scale technology platforms and how they can increase the scalability, availability, performance, and security of data
Able to build collaborative working relationships with peers and senior leadership
Ability to demonstrate strong written, verbal communication and presentation skills to all levels of seniority and disciplines within the organisation
Highly curious, ability to take measured risks and focus on what matters most i.e. 80:20.
Flexibility, ability to plan and organise, responsiveness, creativity, self-starter.