AI Acceleration Lead

GSK•Upper Providence Township, PA

18h

About The Position

This role sits within DP&I (Development Operations Platforms & Innovation), part of Development Global Clinical Operations at GSK. DP&I is building the capability to deploy AI at scale across clinical operations and the wider Development Operations function, with a mandate to help reshape how GSK plans, delivers, and continuously improves clinical trials. DP&I develops and deploys scalable, AI-native capabilities, shared foundations, and new ways of working across Development Operations. Working across clinical operations, technology, product, data, and external partners, the team builds the platforms, products, and partnerships needed to turn innovation into measurable value. As Lead, AI Acceleration, you will support the AI-native Development Operations strategy through the design and delivery of AI pilots that address real operational problems in clinical development. You will work hands-on with clinical operations users, vendors, and enterprise partners to shape hypotheses, test solutions in practice, build evidence of value, and make clear recommendations on what should stop, iterate, or scale. You will help scale solutions from pilot to enterprise adoption and drive measurable impact to clinical development. This role requires structured problem-solving, high ownership, hands-on use of AI tools, and the ability to work effectively across frontline users, technical teams, and senior stakeholders. This role reports into the Head of Asset Acceleration and AI. You will shape how GSK uses AI to measurably accelerate trial delivery and improve outcomes for patients.

Requirements

Bachelor’s degree in Life Sciences, Engineering, Computer Science, Business, or a related field.
4+ years of experience in Pharma strategy, applied AI/technology, transformation, or programme delivery roles.
Experience in clinical operations and wider clinical development, gained in industry, consulting or other relevant settings.
Experience solving ambiguous problems and turning analysis into action.
Experience moving from problem definition through pilot design to clear recommendation, with ownership of delivery end-to-end.
Experience working across users, partners, and cross-functional teams to deliver measurable outcomes in fast-moving, ambiguous environments.
Experience with AI-native development tools, including frontier agentic development tools (e.g. Claude Code, Codex), and rapid prototyping practice.

Nice To Haves

Advanced degree (PhD, MBA, MSc, or equivalent) in a relevant discipline.
Experience in top-tier strategy, venture build, applied AI, or builder-led digital consulting environments.
Knowledge of industry trends and technologies shaping the future of clinical trials.
Experience building business cases, ROI models, or evidence for scaling new capabilities in enterprise settings.
Understanding of GxP expectations relevant to deploying AI in clinical operations.
Experience driving AI-enabled capabilities beyond pilot through to value delivery and scaling at portfolio level.
Exceptional written and verbal communication, with the ability to translate complex strategic and technical topics for senior, non-technical audiences.

Responsibilities

Proactively identify AI opportunities across Development Operations by scanning workflows, engaging clinical operations teams, and spotting where AI can create meaningful value; translate real bottlenecks into clear pilot hypotheses and practical success criteria.
Maintain an active outside-in view of emerging AI capabilities, workflow innovations, and evolving ways of working, and convert those signals into strategic recommendations, roadmap updates, and new opportunity areas.
Scope and run AI pilots end-to-end, including design, delivery approach, execution oversight, and recommendations on whether to stop, iterate, or scale.
Partner with vendors and enterprise technology teams to ensure pilots are robust, compliant, usable, and positioned to scale using shared foundations if successful.
Manage delivery risks, dependencies, and partner performance across active pilots to maintain momentum and execution quality.
Build proof-of-value cases using measures including time saved, quality improvement, user adoption, risk reduction, and readiness to scale.
Use AI-native development tools to run hands-on use-case testing alongside technology teams and business SMEs, validating feasibility and value before committing to full deployment.
Inform portfolio and investment decisions by identifying patterns across pilots, synthesising pilot findings, adoption data, and operational intelligence, and making evidence-based recommendations on what to prioritise, accelerate, reshape, or stop.
Drive adoption for priority AI-native capabilities, including workflow redesign, user readiness, and change interventions required for scaled deployment.
Stakeholder Management: R&D leaders and business owners: shape pilots around real bottlenecks, working practices, and user needs; drive solutions from pilot to scaled adoption. Enterprise AI and technology teams: enable infrastructure, access, integration, and compliant deployment. Vendors and delivery partners: assess capabilities, collaborate effectively, and drive measurable value. Portfolio and functional leaders: use evidence from pilots to inform prioritisation, investment, and scale decisions.