Director, Market Access Operations & Analytics

GSK•Philadelphia, PA

About The Position

Lead GSK’s use of AI to deliver measurable commercial outcomes across Market Access, including account management, pricing, contracting, channel management, trade operations, and payer engagement. Partner with business leaders to understand their work, identify valuable applications for AI and agentic technology, and own delivery end to end—from discovery and development through evaluation, production deployment, and ongoing performance management. The role involves hands-on work with agentic frameworks, LLM-based workflows, and commercial data pipelines. Sitting at the intersection of product, engineering, and commercial strategy, this position 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 shapes GSK’s AI investments across 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 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 acted on by senior leaders.
Experience overseeing technology delivery from design through production deployment, ideally involving 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, rather than only pilots.
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, 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/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 translate pain points into clear problem statements.
Identify and prioritize opportunities for AI and agentic technology across pricing, contracting, channel management, trade operations, and payer engagement, 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 end-to-end delivery through design, build, evaluation, and production deployment, directing data scientists, engineers, UX designers, and vendors and making business-case-aligned build and release decisions.
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 before release, and manage post-launch performance, versioning, change control, failure cases, drift, and continuous improvement based on user feedback.
Build responsible AI practices into solutions, 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, then drive adoption through enablement, training, documentation, and SOP updates.
Turn proven solutions into reusable patterns for other Commercial and D&T teams.