Staff, AI Product Manager

GSK is building toward autonomous manufacturing, and this role sits at the center of that effort. As a Staff AI Product Manager, you will own the manufacturing AI portfolio and roadmap, partner with site and network leadership to choose the right use cases, and guide teams from problem framing through deployment, adoption, and measurable value.

The work is designed to move beyond pilots by defining what autonomy means for GSK sites, scaling proven capabilities across the network, and ensuring teams operate with strong governance in GxP environments.

What you’ll do

• Own the manufacturing AI portfolio and roadmap toward autonomous manufacturing.
• Assess which use cases to pursue quickly, using evidence to support prioritisation and early de-risking.
• Drive end-to-end delivery from problem framing through deployment, adoption, and value realisation.
• Lead change management so AI solutions are used, trusted, and sustained in operational environments.
• Partner with manufacturing leadership on AI-driven transformation.
• Define what autonomous manufacturing means for GSK sites, including the target state by area and a staged, funded roadmap.
• Scale autonomous training content and workforce optimisation programmes to multi-site outcomes.
• Build the business case for autonomous production scheduling and other processes, moving the strongest candidates into delivery.
• Enable reuse of capabilities built at one site, including data foundations, agents, and optimisation engines, across the network.
• Evaluate opportunities on value, data readiness, feasibility, compliance, and site appetite, and stop weak ideas early.
• Create finance-ready business cases covering throughput, OEE, schedule adherence, right-first-time, labour productivity, training cycle time, and cost avoided.
• Maintain a living opportunity view across in-flight, queued, and de-prioritised work with clear rationale, feeding the Enterprise AI portfolio.
• Lead engagements end-to-end with FDE and engineering pods, from scope and success criteria through deployment and handover.
• Manage milestones and dependencies across site, IT/OT, quality, and engineering, clearing blockers from prototype to production.
• Make capacity trade-offs when sponsors compete, pushing back on work that will not move outcomes.
• Build relationships with site, operations, quality, engineering, and learning leaders and help shape their agenda.
• Co-own change management from stakeholder mapping and process redesign to training and adoption tracking.
• Tailor execution by quickly reading each site’s processes, culture, systems, and data.
• Coach Senior AI Product Managers on opportunity selection, value cases, and engagement design.
• Engage VP and site leaders, linking AI progress to manufacturing and patient outcomes.
• Track value realisation using Enterprise AI’s framework and refine the framework as learning accumulates.
• Run post-mortems and feed lessons back into how opportunities are selected, delivered, and scaled.

What you bring

• Education: Bachelor’s degree (or equivalent experience).
• Experience: 12+ years across pharmaceutical, life sciences, or other regulated manufacturing, and AI/ML, digital, or product leadership roles.
• Manufacturing exposure: Experience working at, or directly supporting, a manufacturing production site.
• AI technology experience: Large language models (LLMs), agentic systems, optimisation and scheduling methods, or the ML lifecycle.
• Delivery experience: Experience taking AI or digital initiatives from problem framing through production deployment.
• Change management: Experience leading change management in operational environments.
• Business cases: Experience building business cases for manufacturing or operations initiatives.
• Senior stakeholder influence: Experience as a trusted partner to senior business leaders (VP and site-head level).
• Governance knowledge: Working knowledge of GxP and data-governance constraints in manufacturing, and how they shape AI deployment and validation.
• Strong plus: Experience in production scheduling and optimisation, workforce or shift planning, learning and training systems, MES/ERP, or Industry 4.0 / smart factory programmes.

Technologies you’ll work with

• Large language models (LLMs)
• Agentic systems
• Optimisation and scheduling methods
• ML lifecycle
• MES/ERP (including MES and ERP)

Location and work arrangement

• Location: King of Prussia, PA (hybrid)
• Work model: Hybrid in the United States with scheduled time on-site for collaboration
• Remote: Fully remote work is not available

Compensation

• Salary: USD 171,600 - 286,000 per year

Benefits

• Annual bonus
• Eligibility to participate in share based long term incentive program
• Health care and other insurance benefits (for employee and family)
• Retirement benefits
• Paid holidays
• Vacation
• Paid caregiver/parental and medical leave

Preferred qualifications

• Experience leading AI opportunity discovery and use case triage in manufacturing, including prioritising pipelines, go/no-go decisions, and shaping a staged roadmap toward autonomous manufacturing.
• Experience building value cases and investment recommendations that stand up to finance and senior manufacturing scrutiny, while managing an AI opportunity portfolio.
• Track record of taking AI engagements to production and scaling point solutions into reusable platforms across sites.
• Fluency across LLMs, agentic systems, optimisation, and ML, including the ability to assess manufacturing data readiness (MES, ERP, LMS, historians, rostering systems) and evaluation design.
• Hands-on use of AI tools to automate product management work, including skills, plugins, and comfort with the command-line interface (CLI).
• Experience navigating GxP, validation, and data-governance requirements when designing AI solutions for manufacturing.
• Experience advising VP and site-head leaders, co-leading change management, and communicating AI impact on manufacturing performance, people, and patients.
• Experience tracking value realisation and adoption, running post-mortems, coaching product teams, and challenging assumptions with evidence.