Staff, AI Product Manager

GSK • Upper Providence, PA

1d • $171,600 - $286,000 • Hybrid

About The Position

GSK’s Enterprise AI team turns AI into measurable impact across drug discovery, development, manufacturing, and commercial operations. As Staff (Director), AI Product Manager for Manufacturing, you will own the manufacturing AI portfolio, shape the vision for autonomous manufacturing, and partner with site and network leadership to select, deliver, adopt, and scale AI use cases. The role supports manufacturing sites that make, test, and release medicines and vaccines safely, compliantly, and at scale.

You will scale existing autonomous programmes, including training content management and production-floor workforce allocation, and develop future opportunities such as autonomous production scheduling. The role combines AI understanding, manufacturing experience, execution discipline, and change-management leadership. It requires defining a staged roadmap, assessing opportunities using evidence, guiding initiatives from problem framing through production deployment, and helping business teams own and sustain the change.

This position follows a hybrid working model in the United States, with scheduled on-site time to collaborate with stakeholders. Fully remote work is not available. For candidates based in Cambridge, MA; Waltham, MA; Rockville, MD; or San Francisco, CA, the annual base salary range is $171,600–$286,000. The position also offers an annual bonus and eligibility for a share-based long-term incentive program, depending on role level.

Requirements

• Bachelor’s degree or equivalent experience in technology, product management, business, or life sciences.
• 12 or more years of experience across pharmaceutical, life sciences, or other regulated manufacturing, and AI/ML, digital, or product leadership roles.
• Experience working at or directly supporting a manufacturing production site.
• Experience with AI technologies such as large language models (LLMs), agentic systems, optimisation and scheduling methods, or the ML lifecycle.
• Experience delivering AI or digital initiatives from problem framing through production deployment.
• Experience leading change management in operational environments.
• Experience building business cases for manufacturing or operations initiatives.
• Experience serving as a trusted partner to senior business leaders at VP and site-head level, leading through credibility and influence.
• Working knowledge of GxP and data-governance constraints in manufacturing and how they affect AI deployment and validation.

Nice To Haves

• Experience in production scheduling and optimisation, workforce or shift planning, learning and training systems, MES/ERP, or Industry 4.0 / smart factory programmes.
• Experience leading AI opportunity discovery and use-case triage in manufacturing, prioritising pipelines, making go/no-go decisions, and shaping a staged roadmap toward autonomous manufacturing.
• Experience building value cases and investment recommendations that withstand finance and senior manufacturing scrutiny, and managing an AI opportunity portfolio.
• Track record of taking AI engagements to production, defining scope, success criteria, and exit conditions, 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 across MES, ERP, LMS, historians, and rostering systems, and to design evaluations.
• Hands-on use of AI tools to automate product management work, including skills and 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.

Responsibilities

• Define with leadership the meaning and target state of autonomous manufacturing across GSK sites, and create a staged, funded roadmap.
• Scale autonomous training content and workforce optimisation programmes to achieve multi-site outcomes.
• Build the case for autonomous production scheduling and other processes, advancing the strongest candidates into delivery.
• Make data foundations, agents, optimisation engines, and other capabilities developed at one site reusable across the network.
• Assess use cases for value, data readiness, feasibility, compliance, and site appetite, and stop weak ideas early.
• Develop finance-ready business cases using measures such as throughput, OEE, schedule adherence, right-first-time, labour productivity, training cycle time, and cost avoided.
• Maintain a current view of in-flight, queued, and de-prioritised opportunities, with clear rationale, and feed it into the Enterprise AI portfolio.
• Lead engagements end to end with forward-deployed engineering and engineering teams, from scope and success criteria through deployment and handover.
• Set milestones, manage dependencies across site, IT/OT, quality, and engineering, and remove blockers from prototype to production.
• Make capacity trade-offs among competing sponsors and challenge work that is unlikely to improve outcomes.
• Build relationships with site, operations, quality, engineering, and learning leaders and help shape their agendas.
• Co-own change management, including stakeholder mapping, process redesign, training, and adoption tracking, to support trust and sustained use on the production floor.
• Understand site processes, culture, systems, and data, and tailor the approach to each site.
• Coach Senior AI Product Managers on opportunity selection, value cases, and engagement design.
• Engage VP and site leaders, communicating AI progress in relation to manufacturing and patient outcomes clearly and honestly.
• Track value realisation against each engagement’s original business case using Enterprise AI’s framework, and improve the framework based on experience.
• Conduct post-mortems and apply lessons to opportunity selection, delivery, and scaling.

Benefits

• Annual bonus
• Eligibility to participate in a share-based long-term incentive program, depending on role level
• Health care and other insurance benefits for employees and family
• Retirement benefits
• Paid holidays
• Vacation
• Paid caregiver, parental, and medical leave

What This Job Offers

• Job Type: Full-time
• Career Level: Director