Senior, AI Product Manager (Process Development Science)

In this role, you shape how AI transforms GSK’s process development in the CMC space, partnering with cross-functional teams to identify high-impact opportunities and translate them into actionable AI products. You will balance business value with feasibility, guiding engineers to build scalable solutions that improve manufacturing and drug development outcomes. You’ll lead end-to-end AI initiatives, focusing on self-driving labs, in silico modeling, and platform fitness to drive a 10x transformation. This is a hands-on, impact-driven role within a fast-moving, patient-focused organization.

• learning and development opportunities
• UK-based hybrid role
• impact on healthcare outcomes
• inclusive culture

• Opportunity Identification & Value Sizing: surface AI opportunities, produce concise briefs, and build defensible value cases.
• Strategic Prioritization & Portfolio Shaping: decide what to start/continue/stop and sequence work for maximum impact.
• Engineering Partnership & Enablement: act as day-to-day partner to FDE pods, translate ambiguity into actionable statements and success metrics.
• Stakeholder Alignment & Communication: engage Director-level stakeholders, run decision-driving sessions, provide clear updates and risk assessments.
• Measurement, Learning & Iteration: track value realization and feed lessons back into scoping of future engagements.

• Strong experience in pharma/life sciences, AI/ML product, or strategy roles in technology-led organizations.
• Track record turning complex problems into structured opportunities with defensible business cases.
• Knowledge of process modeling, manufacturing and lab content in R&D.
• Proven experience redesigning business platform thinking for oligonucleotides, ADCs and monoclonal antibodies.
• Experience identifying opportunities in self-driving labs, in silico modeling, and platform fitness for a 10x change.
• Experience designing multi-initiative workstreams with outcome-based delivery.
• Fluency in modern AI/ML to design realistic engagements with engineers.
• Strong individual contributor mindset with high-quality output.

• Stakeholder management and collaboration
• Clear, concise communication
• Decision-making under uncertainty
• AI/ML fluency for engagement design
• Process modeling and data analytics
• Model management and performance evaluation