Staff, AI Product Management (Global Functions)

Job Summary

About the Role:

GSK s Enterprise AI team, a highly skilled applied AI group, translates the promise of AI into measurable impact across every major group at GSK - R&D, Medical, Commercial, Supply Chain, HR, Legal, Finance, and beyond. Bionic pods - AI product managers, forward deployed engineers, full-stack engineers, data engineers, and digital workers - embed directly with scientific and business teams to build AI products that change outcomes.

We are hiring a Staff, AI Product Manager (Director level equivalent role) to own a major slice of our Global Functions portfolio and act as the strategic counterpart to the engineers and digital workers in your pod, setting the forward deployed strategy they execute against. This is a senior individual contributor role for someone who can identify and shape high-value AI opportunities across the key areas of Global Functions - HR, Legal, Compliance, Procurement, or Finance, build the case to fund and deploy against them, and ensure FDEs are working on what matters most. You will not manage engineers; you will shape what they build in your domain and how we measure whether it worked.

Why This Role Exists:

Forward deployed teams are most powerful when paired with sharp opportunity selection. Enterprise AI has no separate strategy layer: the AI Product Manager sets the forward deployed strategy, and the pod executes it. This role closes that gap at this level by ensuring the engineers we deploy are working on the right things.

In this role you will be the person who:

• Owns a clear domain or capability area within the Enterprise AI opportunity portfolio.

• Identifies, sizes, and shapes the highest-value AI opportunities in that domain.

• Partners daily with forward deployed, full-stack, and data engineering leads to scope and steer engagements end-to-end.

• Builds the business and executive alignment the pod needs to land and scale.

• How This Differs from Traditional Product Management:

This is not a traditional enterprise product role managing a source system s roadmap, releases, and backlog. As an AI Product Manager in Enterprise AI, you:

• Build new AI products from ambiguous, unsolved business problems rather than evolving an established platform.

• Work with probabilistic systems - defining evaluations, acceptable error, and human oversight instead of fixed functional requirements.

• Help lead a bionic pod, deciding which work goes to engineers and which to digital workers, and how they hand off.

• Prototype hands on and are measured on business outcomes, not features shipped or system uptime.

Key Responsibilities:

Opportunity Identification & Value Sizing:

• Hunt for high-value AI opportunities within your assigned domain at GSK; develop a structured pipeline of opportunities ranked by impact, feasibility, and time-to-value.

• Quantify value rigorously. Build credible business cases that survive finance and senior business unit scrutiny - cycle time saved, decisions accelerated, revenue uplift, cost avoided, scientific productivity gained.

• Develop deep domain pattern recognition so you can spot AI-shaped problems in workflows the business itself hasn t yet articulated.

Strategic Prioritization & Portfolio Shaping:

• Shape the opportunity portfolio in your domain. Maintain a living view of in-flight, queued, and de-prioritized opportunities, with the rationale for each decision, and feed it into the broader Enterprise AI portfolio.

• Make trade-off recommendations. When two business sponsors both want an FDE pod, defines the success metrics, run the analysis and recommend where engineers go.

• Protect engineering focus. Push back - with evidence - on opportunities that won t move scalable outcomes.

Engineering Partnership & Enablement

• Be the strategic counterpart to forward deployed, full-stack, and data engineering leads in your domain. Translate ambiguous business problems into crisp problem statements, prototypes, success metrics, and decision frameworks engineers can build against.

• Shape engagements end-to-end. Define scope, success criteria, exit conditions, and scaling pathways before kick-off, then stay close as engineers execute.

• Surface re-use and platform leverage when a one-off solution should become a reusable capability.

• Design the human-digital split. Decide which tasks the pod s digital workers own, where engineers and business users stay in the loop, and how evaluation and oversight make that safe.

Stakeholder Alignment & Communication

• Operate fluently with VP-level business and scientific leaders. Run conversations that turn fuzzy ambitions into concrete, fundable AI engagements.

• Build sponsor coalitions that secure data access, change-management commitment, and air cover for your engagements.

• Communicate impact clearly in language that connects AI work to patients, science, and enterprise outcomes.

Measurement, Learning & Iteration

• Apply Enterprise AI s value-realization framework to every engagement in your domain and improve it with what you learn.

• Run honest post-mortems and feed lessons back into how your domain selects opportunities.

Required Functional Experience:

• 18+ years of progressive experience spanning some combination of pharma/life sciences, AI/ML product strategy, and senior product management or strategy roles in technology-led organisations.

• Demonstrated track record of identifying, scoping, prototyping, and shepherding high-value technology or AI initiatives from problem framing through funding into delivery.

• Strong working knowledge of at least one part of the pharma value chain - R&D, clinical, medical, manufacturing, commercial, or an enterprise function - including the data, decisions, and constraints inside it.

• Solid technical fluency in modern AI/ML - LLMs, agentic systems, and digital workers, ML lifecycle, data and evaluation patterns - sufficient to leverage evidence from rapid prototyping to challenge engineers, scope realistically, and avoid hype-driven choices.

• Experience operating as a senior individual contributor - leading through influence, frameworks, and quality of thinking rather than headcount.

• A product management or product-strategy heritage: discovery, opportunity sizing, roadmap shaping, prototyping, and stakeholder alignment - ideally on products whose core behaviour is AI rather than conventional software.

#AGTech

Skills

• AI Platforms
• AI Product Management
• Generative AI

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.