Description
GSK's Enterprise AI team is hiring a Staff, AI Product Manager (Director level equivalent role) to own a major slice of their Global Functions portfolio.
This is a senior individual contributor role for someone who can identify and shape high-value AI opportunities across key areas of Global Functions - HR, Legal, Compliance, Procurement, or Finance, build the case to fund and deploy against them, and ensure forward deployed engineers are working on what matters most.
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, define 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.