Senior AI Product Manager, Market Access

NCBioImpact

Durham (NC)

Sur place

USD 180 000 - 240 000

Plein temps

Il y a 4 jours

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Résumé du poste

GSK invites an experienced AI/ML leader to shape how we use AI to deliver measurable commercial outcomes across Market Access. You will sit with business leaders, define opportunities, and own end-to-end delivery from discovery to production, partnering with data teams and vendors.

This role sits at the intersection of product, engineering, and commercial strategy. You will make feasibility, build-vs-buy calls, and establish governance while delivering accountable, live systems.

Qualifications

10+ years in life sciences or healthcare, with experience in market access commercial operations.
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 - budgets, timelines, quality - on complex programs.
Hands-on experience building and operating agentic AI or LLM-based workflows in production - not just pilots - from idea to measured outcome.
Experience with agile delivery and testing/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

Responsabilités

Partner with Market Access, Commercial Operations, Finance, Tech, and Data teams to turn pain points into clear, specific problem statements.
Identify where AI and agentic technology can create the most value across pricing, contracting, channel management, trade operations, and payer engagement — weighing impact, feasibility, and risk to prioritize investment.
Build business cases with clear expected benefits, costs, and success measures; shape the AI roadmap, balancing quick wins with long‑term capability, and assess build, buy, and partner options.
Own delivery end to end - leading data scientists, engineers, UX designers, and vendors through design, build, evaluation, and production deployment, making key build and release decisions tied back to the business case.
Partner with Data Engineering and commercial data owners to secure the data foundation these products depend on - contract, pricing, rebate, claims, and channel data quality, lineage, and master data alignment.
Set the quality bar for AI solutions - validate before release, then own them after go-live: monitor model, prompt, and agent performance in production, manage versioning and change control, triage failure cases and drift, and run continuous improvement loops from user feedback.
Ensure responsible AI use, with human oversight, documentation, compliance, and governance built in from the start.
Manage budgets, timelines, vendor performance, and delivery risk, flagging issues early.
Prototype and validate solutions in the real world, then drive adoption - enablement, training, documentation, and SOP updates - and convert proven solutions into reusable patterns other Commercial and D&T teams can adopt.