Principal (Senior Director), Commercial Enterprise AI Product Mgmt. - Knowledge Foundation and Insights Acceleration

Position Summary

GSK's Enterprise AI team exists to translate the promise of AI into measurable impact across every major group at GSK — R&D, Medical, Commercial, Supply Chain, HR, Legal, Finance, and beyond. You will act as the strategic compass for driving a portfolio of AI solutions that advance end-to-end insights acceleration across the globe — from primary and secondary market research through competitive intelligence knowledge bases, and the agentic tools that put insights in front of decision-makers — and own the forward deployed strategy each pod executes against.

This is a senior individual contributor role for someone who can see across GSK's value chain, identify where AI will create disproportionate value, prototype hands on, and ensure engineers are deployed against the highest-impact opportunities at all times. You will not manage engineers; you will shape what they build, where they go next, and how we measure whether it worked.

Responsibilities

• Hunt for high-value AI opportunities across the insights acceleration lifecycle — from structuring PMR, SMR, competitive intelligence, and third-party market data (taxonomy, ontology, metadata, common data model, KPI framework) to exposing it through knowledge graphs and agentic AI interfaces — and develop a global portfolio of AI products ranked by impact, feasibility, and time-to-value.
• Quantify value rigorously by building business cases that hold up with finance and executive sponsors — cycle time saved, decisions accelerated, revenue uplift, cost avoided, scientific productivity gained.
• Develop domain pattern recognition in how market, competitive, and HCP/patient data is organized and made usable for insights generation in compliant, secure ways.
• Own the Enterprise AI opportunity portfolio, maintaining a living view of in-flight, queued, and de-prioritized opportunities, with the rationale for each decision.
• Make hard trade-off calls — building the framework, defining success metrics, running the analysis, and recommending where engineers go when business units compete for the same resources.
• Say no, with evidence, to opportunities that look attractive but won't move scalable outcomes, protecting engineering focus.
• Be the strategic counterpart to the forward deployed, full-stack, and data engineering leads, translating ambiguous business problems into crisp problem statements, prototypes, success metrics, and decision frameworks engineers can build against.
• Shape engagements end-to-end — defining scope, success criteria, exit conditions, and scaling pathways before kick-off, then staying close as engineers execute.
• Surface re-use and platform leverage, spotting when a one-off solution should become a reusable capability and working with engineering to make that case.
• Design the human-digital split, deciding 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.
• Operate fluently with senior business and scientific leaders, running executive conversations that turn fuzzy ambitions into concrete, fundable AI engagements.
• Build sponsor coalitions, securing the air cover, data access, and change-management commitment that engineering deployments require to succeed.
• Communicate impact clearly, telling the value story to ELT and the board, in language that connects AI work back to patients, science, and shareholder outcomes.
• Define how we keep score, establishing the value-realization framework Enterprise AI uses to measure impact across deployments.
• Run honest post-mortems — capturing what worked, what didn't, and feeding those learnings back into opportunity selection and engagement design.
• Continuously sharpen the investment thesis on where AI most belongs at GSK, and where it doesn't.

Basic Qualifications

• 15 or more years of professional experience across some combination of pharma/life sciences, AI/ML product strategy, and product management or strategy roles in technology-led organizations.
• Track record of taking at least one technology or AI initiative from problem framing through executive funding and into delivery.
• Hands-on work with the data foundations used in pharma market research and competitive intelligence, including taxonomy, ontology, metadata management, common data model design, or KPI frameworks, as applied to third-party data sets such as IQVIA or Komodo.
• Track record of building or specifying knowledge graphs and conversational or agentic AI interfaces that deliver insights directly to business decisionmakers.
• Hands-on work with modern AI/ML concepts — including LLMs, agentic systems, and digital workers, the ML lifecycle, and data and evaluation patterns — demonstrated through prototyping.
• History of operating as an individual contributor without direct reports and influencing engineering and business teams through frameworks and documented decision criteria.
• Prior responsibility for product management or product strategy, including opportunity sizing, roadmap development, prototyping, and stakeholder alignment.
• Work with the commercial analytics data landscape and the technology capabilities used to operationalize insights at scale.
• Direct involvement in analyzing intelligence sources and delivering insights used in business planning or portfolio decisions.
• Track record of building and maintaining knowledge bases spanning multiple commercial domains.
• Prior responsibility for aggregating regional market intelligence into national or global insights used for decision-making.

Preferred Qualifications

• Prior experience in a forward deployed engineering, applied AI, or AI lab/COE role at a pharma, healthcare, or comparable regulated enterprise.
• Background in management consulting, value engineering, or corporate strategy with hands-on technology experience.
• Experience navigating GxP, regulatory, and data-governance requirements in pharma AI deployments.
• Experience with data foundation and knowledge platforms used in pharma insights, such as MDM/data fabric tools (Reltio, Informatica, Palantir Foundry), graph databases (Neo4j, Amazon Neptune), or vector search/RAG infrastructure.