Director, AI & Technology Transformation

GSK•Upper Providence, PA

1d•Hybrid

About The Position

R&D Tech Transformation & Operations connects strategy, investment and delivery across priority R&D technology programmes, working with scientific, technology and business leaders to strengthen decisions, accelerate value and support consistent adoption across R&D. The Director leads the diagnosis, design and implementation of R&D’s data foundations across seven core elements: data quality, ontology, governance and ownership, data discovery, access and authorization, data integration and platforms, and data products. The role maps end-to-end data journeys, identifies systemic pain points, and translates findings into prioritized interventions supported by clear business cases. It involves engaging R&D leadership and subject matter experts, facilitating cross-functional workshops, reporting progress at RDLT level, and directly owning implementation through to measurable outcomes. The role combines strategic oversight of R&D’s data landscape with hands-on delivery, structured diagnostic thinking, stakeholder influence and execution rigor. For candidates based at Stevenage, the role will temporarily be located there; the company plans to relocate its offices to Cambridge, UK, with the relocation currently proposed to take effect by early 2029. The role offers a hybrid working model through the Performance with Choice programme.

Requirements

Significant experience leading data strategy, data governance or R&D digital transformation initiatives in a pharmaceutical R&D environment.
Demonstrated experience conducting organizational diagnostics, including mapping processes or data journeys, synthesizing pain points and translating findings into actionable recommendations.
Proven track record of taking a strategy from design through implementation and delivery.
Experience designing and facilitating workshops or working sessions that build alignment across senior stakeholders and subject matter experts.
Experience developing and delivering change plans that include stakeholder engagement, readiness, communications and capability building.
Strong analytical skills and ability to assess complex, cross-functional problems and develop a structured, prioritized path forward.
Ability to analyze complex issues, develop practical recommendations and follow through to implementation.
Experience developing business cases, including articulating impact, effort and feasibility trade-offs.
Working knowledge of data management concepts across data quality, ontology, governance and ownership, data discovery, access and authorization, data integration and platforms, and data products.
Strong communication, facilitation and influencing skills across technical and non-technical stakeholders.
Ability to work independently, manage ambiguity and contribute beyond a narrowly defined change-management remit.
Experience defining measures for adoption, outcomes or value realization and using data to inform decisions.

Nice To Haves

Experience with R&D data platforms.
Background in management consulting combined with in-house strategy and transformation delivery implementation.
Change-management, project-management or related professional certification.
Experience building and leading cross-functional core teams or workstreams through to delivery.
Familiarity with data governance frameworks, IHD/consent-related data access processes or regulatory data considerations.
Experience managing delivery-focused teams or programs with direct accountability for outcomes.
Learning, capability-building or workforce upskilling experience.

Responsibilities

Lead comprehensive diagnostics of R&D data journeys, mapping end-to-end flows from scientific question to data-driven decision across systems, users and data.
Identify and synthesize pain points across all seven data elements, distinguishing journey-specific issues from systemic, enterprise-wide gaps.
Engage 50+ subject matter experts and core team members across Research, Development, Tech, and AI/ML to ground findings in real user experience.
Define a future-state ambition for priority data-management capabilities.
Run sessions with R&D leadership and design and facilitate cross-functional workshops to align stakeholders on the future state and foundational interventions.
Develop and prioritize interventions based on R&D impact, breadth of reuse and organizational feasibility, focusing on cross-cutting strategic moves.
Build business cases for priority interventions, including expected impact, effort or cost, and implementation considerations.
Report progress and recommendations at RDLT level and secure leadership endorsement for the roadmap.
Take direct ownership of implementation and lead delivery of prioritized interventions end to end through to measurable outcomes.
Establish the workstreams, governance and ownership structures needed to advance interventions.
Identify where existing platforms or initiatives can be leveraged and where new capabilities are required.
Develop and deliver change plans that include stakeholder engagement, readiness, communications and capability building.
Define measures for adoption, outcomes or value realization and use data to inform decisions.