Director, Market Access Operations & Analytics

GSK•Philadelphia, PA

About The Position

GSK is seeking a leader to determine how AI and agentic technology can deliver measurable commercial outcomes across Market Access, including account management, pricing, contracting, channel management, trade operations, and payer engagement. The role partners directly with business leaders to understand operational needs, identify high-value opportunities, and lead delivery from discovery through build, evaluation, production deployment, and ongoing performance management. This position operates at the intersection of product, engineering, and commercial strategy. The Director 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 by adoption and quantified commercial impact. The role also helps shape GSK’s AI investment priorities for Market Access in support of the company’s mission to unite science, technology, and talent to get ahead of disease together.

Requirements

10+ years of experience in life sciences or healthcare, including Market Access commercial operations experience in 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 acted on by senior leaders.
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 idea to 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 clearly explaining complex ideas.

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 or workflow 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, Tech, and Data teams to translate 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 that define expected benefits, costs, and success measures; shape the AI roadmap and assess build, buy, and partner options.
Lead end-to-end delivery, guiding data scientists, engineers, UX designers, and vendors through design, build, evaluation, and production deployment, with 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.
Set quality standards for AI solutions, validate them before release, and manage post-launch performance, versioning, change control, failure cases, drift, and continuous improvement based on user feedback.
Ensure responsible AI practices, including 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, drive adoption through enablement, training, documentation, and SOP updates, and turn proven solutions into reusable patterns for other Commercial and D&T teams.