Senior Enterprise AI Strategist

GSK; Philadelphia (Philadelphia County); On-site; USD 196,000 - 326,000; Full time

Job summary

GSK is expanding its Enterprise AI team to translate AI promises into measurable impact across R&D, Medical, Commercial, and more. You will identify high-value AI opportunities, prototype and guide engineers, and shape scalable, reusable AI capabilities with strong governance and value-based decision making.

Senior individual contributor role focusing on opportunity sizing, prototyping, and executive alignment, operating without direct reports while influencing cross-functional teams and leaders.

Qualifications

• 15 or more years of professional experience across 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 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.

Responsibilities

• Hunt for high-value AI opportunities across the insight's acceleration lifecycle — from structuring PMR, SMR, competitive intelligence, and third-party market data to exposing it through knowledge graphs and agentic AI interfaces.
• 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.

Skills

AI Platforms

Change Management

Data Analysis

Feature Prioritization

ML Platforms

Model Evaluation

Performance Analysis

Product Delivery

Product Design

Product Lifecycle

Roadmap Development

Roadmapping

Statistical Analysis

User Needs Analysis

Job description

GSK is expanding its Enterprise AI team to translate AI promises into measurable impact across R&D, Medical, Commercial, and more. You will identify high-value AI opportunities, prototype and guide engineers, and shape scalable, reusable AI capabilities with strong governance and value-based decision making.

Senior individual contributor role focusing on opportunity sizing, prototyping, and executive alignment, operating without direct reports while influencing cross-functional teams and leaders.