Sr. AI Product Manager – Market Access

GSK•Philadelphia, PA

17h

About The Position

GSK is seeking a Senior AI Product Manager to lead the use of AI and agentic technology to deliver measurable commercial outcomes across Market Access, including account management, pricing, contracting, channel management, trade operations, and payer engagement. The role works closely with business leaders to understand their workflows, identify high-value opportunities, and own product delivery end to end—from discovery and feasibility assessment through build, evaluation, production deployment, and ongoing performance management. The position operates at the intersection of product, engineering, and commercial strategy. The role makes feasibility and build-versus-buy decisions, defines quality standards for non-deterministic AI systems, and moves solutions from proof of concept into governed, monitored production use. Success is measured by adoption and quantified commercial impact. The role also helps shape GSK’s AI investment in Market Access in support of the company’s mission to unite science, technology, and talent to get ahead of disease.

Requirements

10+ years of experience in life sciences or healthcare, including Market Access commercial operations such as account management, pricing, contracting, channel management, trade operations, or payer engagement.
Bachelor’s degree in business, economics, data science, computer science, public health, or life sciences.
Experience translating complex business problems into programs that deliver measurable results.
Experience setting priorities and building roadmaps or investment cases that senior leaders acted on.
Experience overseeing technology delivery from design through production deployment, ideally including AI or advanced analytics.
Experience managing delivery teams and vendors, including budgets, timelines, and quality, on complex programs.
Hands-on experience building and operating agentic AI or LLM-based workflows in production, from initial concept to measured outcome.
Experience with agile delivery and testing or validation in regulated environments.
Technical knowledge to engage data scientists and vendors on model and agentic workflow evaluation, monitoring, and governance.
Experience building trusted relationships with senior stakeholders and explaining complex ideas clearly.

Nice To Haves

Advanced degree (MBA, MSc, MPH, or PhD) in a relevant discipline.
Direct experience building AI solutions for Market Access operations, real-world evidence, or health technology assessment.
Experience designing or operating agentic AI or automation platforms, such as LLM orchestration frameworks, workflow builders, or agent builders, in a regulated enterprise setting.
Familiarity with cloud AI platforms such as Azure AI, Google Cloud, or AWS, and with MLOps/LLMOps concepts.
Experience with human-in-the-loop systems, prompt engineering, or deploying generative AI in regulated environments.
Experience assessing AI vendors or platforms.
Experience leading transformation, merger integration, or operating model change.

Responsibilities

Partner with Market Access, Commercial Operations, Finance, Technology, and Data teams to turn business pain points into clear problem statements.
Identify and prioritize AI and agentic technology opportunities across pricing, contracting, channel management, trade operations, and payer engagement, considering impact, feasibility, and risk.
Build business cases with expected benefits, costs, and success measures; shape the AI roadmap and assess build, buy, and partner options.
Lead end-to-end delivery through design, build, evaluation, and production deployment, coordinating data scientists, engineers, UX designers, and vendors and making build and release decisions tied to the business case.
Partner with Data Engineering and commercial data owners to establish data quality, lineage, and master data alignment for contract, pricing, rebate, claims, and channel data.
Define quality standards for AI solutions and validate them before release; monitor model, prompt, and agent performance after launch; manage versioning and change control; triage failures and drift; and run continuous-improvement cycles based on user feedback.
Build responsible AI practices into solutions, including human oversight, documentation, compliance, and governance.
Manage budgets, timelines, vendor performance, and delivery risk, and raise issues early.
Prototype and validate solutions in real-world settings, drive adoption through enablement, training, documentation, and SOP updates, and turn proven solutions into reusable patterns for other Commercial and D&T teams.