Manager, Translational Biomarker Data Pathways & Re-use

GSK•Upper Providence, PA

7d•Hybrid

About The Position

The R&D Translational Sciences (TS) team at GSK is a world-class centre of excellence in genetics, genomics and translational science focused on generating genetic and genomic insights into Research Unit-directed disease and product strategies. Our operating principles include end-to-end translation, strong integration between translational leads and core functional teams, and excellence in foundational capabilities while increasing earlier translational impact. Reporting to the Senior Director, Translational Samples & Informatics, this role provides operational leadership to set up, stabilise and scale end-to-end (E2E) translational data pathways, including enabling re-use of clinical datasets at scale to support translational research and decision-making. A key objective of this role is to help evolve translational biomarker datasets from one-off outputs into reusable, accessible data assets; available, interpretable and usable across multiple translational use cases. The role operates across a spectrum of technology and system maturity: providing hands-on leadership where processes are new or fragmented, and building repeatable, scalable ways of working as systems mature, transitioning into business as usual with matrix partners. This role delivers Translational Sciences and GSK pipeline objectives by connecting and aligning delivery across internal and external partners (including R&D Digital & Tech (RDDT), Platform Sciences, Research & Development Technologies (R&D Tech), Biostatistics & Programming, and AIML), to support impactful use and re-use of human data for translational activities.

Requirements

MSc (or equivalent) in relevant discipline.
Significant experience working with human biomarker or high-dimensional data types in Pharma/Biotech, academia, healthcare, or a similar complex R&D environment.
Ability to solve complex, cross-functional data and/or technology challenges, using a structured and collaborative approach
Strong stakeholder management skills in matrix organisations
Working knowledge of data governance and operational data flows (for example FAIR and data integrity principles)
Strong organisational and prioritisation skills
Ability to lead in areas of ambiguity, bringing clarity and direction while partnering effectively with others

Nice To Haves

Experience working in multi-cloud and/or multi-platform environments
Experience with high-dimensional biological data types, their generation and/or analysis (for example RNA-seq, spatial transcriptomics, proteomics, digital imaging, flow cytometry)
Experience working with human and/or clinical data in a regulated environment (for example navigating privacy, consent, and/or global legislation impacting data use and re-use)
Evidence of cross-organisational data leadership contributing to delivery of a high-impact, cross-matrix data initiative
Understanding of the drug discovery and development lifecycle
Experience producing process documentation and/or defining workflows
Coding and/or data manipulation skills (Python and/or R)

Responsibilities

Lead the end-to-end enablement of biomarker dataset re-use (clinical & non-clinical) by helping ensure data is findable, accessible, interpretable and aligned to translational decision-making timelines.
Identify and drive resolution of systemic barriers (for example: unclear ownership, access constraints, metadata gaps and platform fragmentation).
Establish repeatable, scalable end-to-end (E2E) pathways.
When needed, lead the set-up and delivery of complex end-to-end biomarker data pathways across multiple functions, defining data flows, roles, responsibilities and dependencies.
Support the transition of pathways into stable, repeatable models, and codify ways of working into standard processes, templates and reusable patterns.
Align data delivery or access with analysis readiness and translational decision milestones to maximise impact for GSK’s portfolio.
Act as an operational focal point for Translational Sciences data flows, helping ensure delivery aligns with enterprise governance frameworks and working in partnership with Data Automation and Predictive Sciences, Platform Sciences Data Operations and the Development Data Value Office.
Reduce reliance on senior intervention to align teams and resolve data pathway challenges.
Develop and maintain visibility of active Translational Sciences data assets, pathways and re-use opportunities to proactively identify risks, opportunities and priorities.
Monitor data-related performance and drive improvements to increase reliability, accelerate progress and reduce manual intervention.