AI product manager in process development

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.

• You’ll help shape how AI transforms GSK’s process development 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 AI opportunities in the process development science for R&D.
• You will redesign the business platform thinking for oligonucleotides (oligos), antibody-drug conjugates (ADCs) and monoclonal antibodies, and foster our self-driving labs initiatives to automate the physical space.
• This will support in silico modeling and platform fitness, especially in a 10x transformation.
• You will work hand in hand with the data analytics team in process science and partner with the R&D Tech teams, and our shared focus is to bring a 10x change in the way we do modeling, optimization, agentic workflows, document authoring and physical AI for simulation.
• 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.
• 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.
• 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.
• 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.
• 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.

• 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 process modeling, manufacturing and laboratory content in R&D, including how decisions actually get made on the ground.
• Proven experience redesigning the business platform thinking for drug modalities such as oligonucleotides, antibody-drug conjugates (ADCs) and monoclonal antibodies.
• Proven experience identifying opportunities in self-driving labs to automate the physical space, and across in silico modeling and platform fitness, and shaping them to deliver a 10x change.
• Proven experience designing the strategy of multi-initiative workstreams: capturing opportunities embedded in the business, sizing value, and owning outcome-based delivery.
• Comfortable technical fluency in modern AI/ML — enough to design realistic engagements with engineers and avoid hype-driven choices.
• Strong individual contributor instincts — you produce quality artefacts, not status updates.
• Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experience.
• Будет плюсом: Experience in process development or manufacturing for oligonucleotides, ADCs or monoclonal antibodies; Experience with self-driving labs and lab automation of the physical space, in silico modeling, or platform fitness products and programs; 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; Master’s degree in Computer Science or related field is advantageous

• You will join a supportive team that values clear communication and practical problem solving.
• You will have access to learning and development to grow both technical and leadership skills.
• Your work will support technology that enables critical work across the company and helps improve healthcare outcomes.