Manager II, AI Product

About the Role GSK’s Enterprise AI team translates the promise of AI into measurable impact across drug discovery, development, manufacturing, and commercial. Forward deployed engineers (FDEs) and full-stack engineers embed directly with scientific and business teams to build AI products that change outcomes. We are hiring a Product Manager II, AI Product Manager to support our forward deployed teams working with GSK’s manufacturing sites. This is an individual contributor role for someone early- to mid-career who is hungry to do the analytical, hands-on, and stakeholder-facing work that makes AI engagements succeed: opportunity discovery, value sizing, scoping, hands-on prototyping, delivery, and ongoing measurement. You will be supported by senior team members, but the work — the briefs, the analyses, the value cases, the scoping docs, and getting things over the line — will be yours to drive. This role is execution first. You will work directly with site teams in production, planning, quality, and training, and iterate with them on the data, the model outputs, and the workflow until the solution works on the floor. You are comfortable rolling up your sleeves: pulling and checking data, testing outputs, running user sessions, and closing the loop between the site and the engineers.   Why This Role Exists Forward deployed teams are most powerful when paired with sharp opportunity selection and disciplined execution. This role closes that gap at this level by ensuring the engineers we deploy are working on the right things, and that what they build lands with the people at the site who will use it. In this role you will be the person who: •     Drives opportunity discovery and value-case work on specific manufacturing engagements, supported by senior team members. •     Acts as a working partner to FDE pods on day-to-day scoping, delivery, and measurement. •     Works hands-on with site stakeholders, iterating on data, model outputs, and workflows until solutions are fit for use. •     Builds the analytical artefacts (briefs, value models, scoping docs, early prototypes) the team relies on. Key Responsibilities Opportunity Identification & Value Sizing •   Run opportunity discovery with site and business stakeholders — interviews, workflow walk-throughs, time on the production floor, light data analysis — to surface AI-shaped problems worth pursuing. •   Build first-cut value cases with clear assumptions, rough sizing, and a credible path to validation, using manufacturing metrics such as throughput, schedule adherence, right-first-time, labour productivity, and training cycle time. •   Document opportunities in a consistent, comparable format so they can be ranked and prioritized. Strategic Prioritization & Portfolio Shaping •   Help senior team members prioritize by gathering the evidence, building the comparison views, and pressure-testing assumptions. •   Track the opportunity backlog and keep it tidy, current, and decision-ready. Execution & Hands-On Delivery •   Own the execution of your assigned workstreams: plans, actions, dependencies, and follow-through, so engagements keep moving from prototype to production. •   Get hands-on when needed: pull, explore, and sanity-check site data; build quick analyses or low-code prototypes; test model outputs against what the site expects. •   Iterate with site stakeholders on data quality, model outputs, and user feedback. Run review and user-testing sessions, capture what is wrong or missing, and turn it into clear asks for the engineers. •   Support site rollout and adoption: user guides, training sessions, feedback loops, and tracking whether the solution is actually being used. Engineering Partnership & Enablement •   Be a working partner to FDE pods. Sit close to the work, captu