Sr. AI Product Manager – Market Access

GSK•Philadelphia, PA

About The Position

The Sr. AI Product Manager will lead the use of AI to deliver measurable commercial outcomes across Market Access, including account management, pricing, contracting, channel management, trade operations, and payer engagement. The role partners with business leaders to understand their work, identify opportunities for AI and agentic technology, and own delivery from discovery through build, evaluation, production deployment, and ongoing performance management. The role involves hands-on work with agentic frameworks, LLM-based workflows, and commercial data pipelines. Working at the intersection of product, engineering, and commercial strategy, the role makes feasibility and build-versus-buy decisions, defines quality measures for non-deterministic AI systems, and moves solutions from proof of concept into governed, monitored production use. Success is measured through adoption and quantified commercial impact. The role works with Market Access, Commercial Operations, Finance, Technology, Data, and delivery teams to define business problems, prioritize investments, create roadmaps and business cases, ensure strong data foundations, and govern AI quality and responsible use. It also manages budgets, timelines, vendors, and delivery risks; supports adoption through enablement and process updates; and turns validated solutions into reusable patterns.

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 idea through measured outcome.
Experience with agile delivery and testing or validation in regulated environments.
Technical knowledge sufficient 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, 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 or workflow or agent builders, in a regulated enterprise setting.
Familiarity with cloud AI platforms such as Azure AI, Google Cloud, or AWS, and 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 by weighing 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 delivery teams and vendors through solution design, build, evaluation, and production deployment, 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, validate them before release, and manage production performance, versioning, change control, failure cases, drift, and continuous improvement.
Ensure responsible AI use by building in human oversight, documentation, compliance, and governance.
Manage budgets, timelines, vendor performance, and delivery risks, escalating issues early.
Prototype and validate solutions in real-world settings, then drive adoption through enablement, training, documentation, and SOP updates.
Convert proven solutions into reusable patterns for other Commercial and D&T teams.