Senior, AI Product Manager (Tech Transfer & Strategy)

Job description

Senior, AI Product Manager (Tech Transfer & Strategy) About GSK Enterprise AI Enterprise AI is GSK’s centre of gravity for applied artificial intelligence. We pair forward deployed engineers and full-stack engineers with scientific, clinical, and business teams to build AI products that get used — not pilots that get archived. We work fast, ship in production, and measure ourselves on outcomes that matter to patients. GSK is a global biopharma company with a purpose to unite science, technology, and talent to get ahead of disease together. We are committed to building a diverse and inclusive team — we welcome candidates from all backgrounds and underrepresented groups in technology, science, and pharma. Position Summary GSK’s Enterprise AI team translates the promise of AI into measurable impact across drug discovery, development, manufacturing, and commercial. Forward deployed teams embed directly with scientific and business teams to build AI products that change outcomes. This specific role sites in the Chemical Manufacturing & Control (CMC) area. This is the GSK function that turns a validated molecule into a reliably manufactured, quality-assured medicine; its mission is to get safe, effective, consistently manufactured medicines to patients, faster. Its current mission is to double New Product Introductions (NPIs). As a Senior AI Product Manager, you’ll help shape how AI transforms GSK’s manufacturing sites around the world: identifying where it will deliver the greatest value and keeping our teams always focused on the highest-impact opportunities. You’ll partner closely with business stakeholders to prioritize opportunities based on feasibility and value. You’ll take a complex business problem, structure it, size the value, prototype hands-on, and turn it into something a forward deployed engineer (FDE) can build against. You will run the day-to-day tech transfer engagement with sites and Contract Manufacturing Organizations (CMOs), and redesign processes at a detailed level. You will shape the strategy to optimize NPI launches, covering site impact assessment, capacity management, digital twins, scenario planning and virtual commissioning of lines, with the goal of one-click deployment of a new line and product. You will define the target architecture, own the tech transfer package, and drive the AI-native end-to-end process redesign. Key Responsibilities This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following: Opportunity Identification & Value Sizing Surface and size AI opportunities across your assigned engagements and adjacent business units; produce one-page opportunity briefs that are easy to compare and prioritize. Build credible value cases with finance-grade reasoning behind cycle time saved, decisions accelerated, revenue uplift, or cost avoided. Develop hands-on domain knowledge in your engagement areas — workflows, data, decisions, and the people who own them. Strategic Prioritization & Portfolio Shaping Recommend what to start, continue, and stop across your engagements based on evidence, not enthusiasm. Sequence work for impact. Help engineering leads decide what is in scope this sprint, what is next, and what is out. Tech Transfer Strategy & Agentification Define and drive the New Product Introduction (NPI) launch strategy optimization — identify the bottlenecks and levers that shorten time from late-stage development to commercial supply, and quantify the impact on launch timelines. Lead the architecture definition for AI-enabled tech transfer — the target process, data and agent architecture connecting development, sites, Manufacturing Science and Technology (MSAT) and supply chain, in partnership with FDE pods and enterprise architecture. Own the definition of the tech transfer package — the content, structure, data and knowledge it must carry — and how it is generated, validated and kept current in an AI native way. Drive agentification across the end-to-end tech transfer process — decide which steps are automated by agents, which keep humans in the loop, and how agents integrate with site systems (Manufacturing Execution System (MES) and others). Lead recipe building for new products — translate process knowledge and the tech transfer package into structured, executable manufacturing recipes (e.g. master and control recipes, ISA-88 style) ready for MES, using agents to draft, check and adapt recipes to each site's equipment and constraints. Engineering Partnership & Enablement Be the day-to-day strategic partner to FDE pods. Convert business ambiguity into crisp problem statements, success metrics, and decision frameworks engineers can act on. Run engagement structure end-to-end. Scope, success criteria, exit conditions, scaling pathways — drafted, agreed, and revisited. Spot reusable patterns that should become platform capability instead of point solutions. Stakeholder Alignment & Communication Operate fluently with Director-level business and scientific stakeholders. Run working sessions that move things forward and produce decisions, not just notes. Maintain sponsor confidence with concise updates, honest risk calls, and clear asks. Measurement, Learning & Iteration Track value realization on each engagement against the case you wrote up front. Capture lessons rigorously and feed them back into how the team scopes the next engagement. Why You! We are seeking professionals with the following required skills and qualifications to help us achieve our goals: Required Experience Strong progressive experience in some combination of pharma/life sciences, AI/ML product, product management, or strategy roles in technology-led organisations. A track record of taking complex, ambiguous business problems and turning them into structured, sized opportunities with defensible business cases. Working knowledge of GSC and technology transfer processes (sites, MSAT, supply chain), including how decisions actually get made on the ground. Proven experience engaging sites and operational teams day to day, building trusted relationships and turning their pain points into AI-shaped opportunities. Comfortable technical fluency in modern AI/ML — enough to design realistic engagements with engineers and avoid hype-driven choices. Proven experience redesigning business processes at a detailed, step-by-step level in an AI native way, reaching 10x transformation. Strong individual contributor instincts — you produce quality artefacts, not status updates. Foundations in product management discovery and value framing. Preferred Experience Experience working with site MSAT, technology transfer leads, or manufacturing operations teams. Experience with Manufacturing Execution Systems (MES) and adjacent site systems (e.g. Laboratory Information Management System (LIMS), Enterprise Resource Planning (ERP), Quality Management System (QMS), historians, electronic batch records), and an understanding of how their data feeds tech transfer and launch readiness. Experience building and authoring manufacturing recipes in MES and automation platforms (e.g. PAS-X, Syncade, DeltaV), and in managing recipe changes across sites. Familiarity with DTLS and similar technologies — digital twin, process modeling and simulation tools — and how they inform recipe design, scale-up and tech transfer decisions. Experience working with data analytics, data science, or process science teams to bring a 10x change in modeling, optimization, and simulation. Experience in regulated industries (pharma, healthcare, financial services) and familiarity with good practice regulations (GxP) and data-governance constraints. Prior experience as an AI product manager at an AI-native company (Anthropic, Palantir, OpenAI, or similar) or at a hyperscaler. Education Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experien