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

GSK • Philadelphia, PA

3d

About The Position

GSK’s Enterprise AI team translates AI opportunities into measurable impact across functions including R&D, Medical, Commercial, Supply Chain, HR, Legal, and Finance. This senior individual contributor will set strategy for a portfolio of AI solutions that accelerate insights globally, from primary and secondary market research and competitive-intelligence knowledge bases to agentic tools that put insights in front of decision-makers. The role shapes what forward-deployed engineering teams build, where they focus, and how outcomes are measured; it does not manage engineers.

The position identifies, sizes, and prioritizes AI opportunities, tests proposed engagements for value, feasibility, and strategic fit, and develops the business case, framing, prototypes, and executive alignment needed to launch and scale them. It owns the opportunity portfolio and value-realization framework, works closely with engineering and business leaders, and communicates the connection between AI outcomes, patients, science, and shareholder value.

Requirements

• At least 15 years of professional experience across some combination of pharma/life sciences, AI/ML product strategy, and product management or strategy 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 experience with pharma market-research and competitive-intelligence data foundations, including taxonomy, ontology, metadata management, common data model design, or KPI frameworks, applied to third-party datasets such as IQVIA or Komodo.
• Track record of building or specifying knowledge graphs and conversational or agentic AI interfaces that deliver insights to business decision-makers.
• Hands-on prototyping experience with modern AI/ML concepts, including LLMs, agentic systems, digital workers, the ML lifecycle, and data and evaluation patterns.
• Experience as an individual contributor without direct reports, 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.
• Experience with commercial analytics data landscapes and 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 across multiple commercial domains.
• Prior responsibility for aggregating regional market intelligence into national or global insights used for decision-making.

Nice To Haves

• 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 combined with hands-on technology experience.
• Experience navigating GxP, regulatory, and data-governance requirements in pharma AI deployments.
• Experience with pharma insights data-foundation and knowledge platforms, such as Reltio, Informatica, Palantir Foundry, Neo4j, Amazon Neptune, or vector-search/RAG infrastructure.

Responsibilities

• Identify and prioritize high-value AI opportunities across the insights-acceleration lifecycle, including market research, competitive intelligence, and third-party market data foundations, knowledge graphs, and agentic interfaces.
• Develop a global portfolio of AI products ranked by impact, feasibility, and time-to-value.
• Build rigorous business cases and quantify value, such as time saved, decisions accelerated, revenue uplift, costs avoided, and scientific productivity gained.
• Develop domain expertise in organizing market, competitive, and HCP/patient data for compliant, secure insights generation.
• Own and maintain a portfolio view of in-flight, queued, and de-prioritized opportunities, including the rationale for decisions.
• Create decision frameworks and success metrics, analyze trade-offs, and recommend how engineering resources should be allocated across competing business-unit needs.
• Use evidence to reject opportunities that are unlikely to produce scalable outcomes and protect engineering focus.
• Translate ambiguous business problems into clear problem statements, prototypes, success metrics, and decision frameworks for engineering teams.
• Shape engagements end to end by defining scope, success criteria, exit conditions, and scaling pathways, and remain closely involved during execution.
• Identify opportunities for reuse and platform leverage, and work with engineering to develop reusable capabilities from one-off solutions.
• Design the division of work between digital workers, engineers, and business users, including evaluation and oversight approaches.
• Lead executive conversations that turn broad ambitions into concrete, fundable AI engagements.
• Build sponsor coalitions and secure executive support, data access, and change-management commitment for deployments.
• Communicate impact to executive leadership and the board in terms connected to patients, science, and shareholder outcomes.
• Establish the value-realization framework for measuring impact across Enterprise AI deployments.
• Conduct post-mortems and use lessons learned to improve opportunity selection and engagement design.
• Continuously refine the investment thesis for where AI is and is not appropriate at GSK.

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: Senior
• Education Level: No Education Listed