Staff, AI Product Manager

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 AI agents and workers — embed directly with scientific and business teams to build AI products that change outcomes.

We are hiring a Staff, AI Product Manager to own Emerging Tech and AI Tooling, the front edge of GSK’s AI platform estate. You will run structured evaluation of new AI tools and platforms, decide what graduates into a standing tower and what gets retired, and own the AI developer tooling GSK engineers use every day. It suits someone who can hold an evaluation to a decision date and retire tools the organisation is attached to. You will lead a small engineering squad and set direction a far larger estate inherits.

Why This Role Exists

Every week brings a new AI tool and a stakeholder who wants it. Without a disciplined front door, evaluations never close and nothing is ever retired. This role runs that front door, and owns the developer tooling estate so the tools engineers depend on daily have a named owner rather than a shared inbox.

In this role you will be the person who

• Owns the evaluation portfolio for new AI tools and platforms across Enterprise AI.

• Decides what graduates into a standing platform tower, what holds, and what gets retired.

• Owns the AI developer tooling estate end to end.

• Hands graduated platforms over with a working operating model.

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.

• Work with probabilistic systems, defining evaluations, acceptable error, and human oversight.

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

• Prototype hands on and are measured on business outcomes.

Key Responsibilities

Evaluation & Graduation

• Run every evaluation to a decision. Set the hypothesis, the success bar, the comparison set, and the kill date up front. Keep a live view of what is in flight, on hold, or retired.

• Quantify value rigorously. Build credible adoption and retirement cases that survive finance and architecture scrutiny — engineering hours saved, licence cost avoided, capability gained, duplication removed.

• Tell new capability from repackaging so overlap with what GSK already runs is caught before it becomes duplicate spend.

Strategic Prioritization & Portfolio Shaping

• Shape the emerging tech portfolio. Keep a living view of in-flight, queued, and retired evaluations with the rationale for each call, and feed it into the AI Platforms & Tools portfolio.

• Make the graduate, retire, or hold call. Decide which tower receives a graduating platform, and hold the decision date when the organisation would rather extend it.

• Protect the estate from sprawl. Say no, with evidence, to tools that look attractive but do not change what the organisation can actually do.

Engineering Partnership & Enablement

• Lead the Emerging Tech and AI Tooling squad. Set direction for a small full-stack team across evaluation builds and the tooling estate, and stay close enough to review the work.

• Own the graduation handover. Hand the receiving tower an operating model, support path, and entitlement design.

• Own the AI developer tooling estate. Run access, entitlement, support, and adoption for the coding and desktop tools engineers depend on daily.

• Stay hands on. Prototype alongside the team and form your own judgment on a tool by using it.

Stakeholder Alignment & Communication

• Operate fluently with VP-level technology and business leaders. Run conversations that turn vendor enthusiasm into a testable hypothesis with a decision date.

• Build sponsor coalitions that secure evaluation funding, pilot cohorts, and the air cover to retire tools people are attached to.

• Communicate impact clearly in language that connects tooling investment to engineering throughput and enterprise outcomes.

Measurement, Learning & Iteration

• Apply Enterprise AI’s value-realization framework to every evaluation and graduation and improve it with what you learn.

• Run honest post-mortems on evaluations that failed, and feed lessons back into how the front door selects what to test.

Required skil ls

• 16+ 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 enterprise AI platforms and developer tooling — how they are licensed, entitled, supported, and adopted at scale — and of at least one part of the pharma value chain.

• Solid technical fluency in modern AI/ML — LLMs, agentic systems, AI agents and 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 leading a small engineering team while operating with a span of influence far wider than the headcount — leading through frameworks, evidence, and quality of thinking.

• 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.

Preferred skills

• Hands on with the AI platform ecosystem as a da