Associate Director, Translational Omics

At GSK, we unite science, technology, and talent to get ahead of disease together. Our goal is to improve the lives of billions across the world. By bringing together our outstanding people, we can make an impact on a global scale.

GSK is seeking a motivated computational biologist to focus on analysis and integration of diverse and complex multi-omic data to deliver actionable insights that will help bring the right medicines to the right patients. Your work will have application across our pipeline, from multi-omic characterisation of disease endotypes to biomarker discovery and patient stratification, with a focus on hepatology, renal and respiratory disease. The successful candidate will be based in our growing Asia R&D organization in our Shanghai office and will align to our global R&D Translational Sciences organisation.

In this role, you will partner closely with local, regional, and global colleagues across R&D Translational Sciences, Early Pipeline Unit and Clinical Development to design and deliver strategic, cutting-edge, and scientifically robust translational and clinical biomarker data analytics, aligned with global research & development strategies. You will bring expertise in the analysis and integration of patient level multi-omic data to deliver insights that impact biomarker discovery, disease endotyping and patient selection.

Principal accountabilities for the role will be focused on delivery in Asia, but you will also align to the global R&D Translational Sciences team and shape our translational strategy and delivery. There will also be the opportunity to spend time in our European and US hubs to develop and deliver that global strategy.

This role supports end-to-end disease area translational omics analytics to help deliver therapy area and program level translational and biomarker strategies (from preclinical to launch) to increase the probability of success from preclinical to clinical transitions and enable earlier and higher confidence asset related decisions. This includes analysis of foundational disease data to understand endotypes and mechanisms of disease, analyses to provide novel data-driven tools to support disease biomarker development and validation within clinical studies, pairing mechanisms of action (MoA) with patient subtypes to predict and evaluate responder populations and reverse translation to impact earlier stage discovery.

Key responsibilities:

Impact the GSK drug development pipeline through development and application of innovative computational and statistical approaches to the analysis, integration and interpretation of spatial, single cell and other cutting edge high dimensional multi-omic data.

Help drive the evaluation and development and lead the application of analytics methods for multi-omics in the context of clinical studies.

Partner and collaborate within cross-functional teams including biology, translational and clinical to define, prioritise and deliver translational computational biology insights to impact pipeline progression and reverse translation across respiratory, hepatology or renal disease.

Effectively communicate analysis findings and recommendations, with expert interpretation, to project teams.

Work with focus and agility to deliver against objectives, demonstrating strong statistical, analytical and critical thinking skills.

Basic qualifications:

PhD or equivalent experience in computational biology, bioinformatics, computational sciences or machine learning.

Extensive experience in the analysis of multi-omic data types, including single-cell and spatial omics datasets with proven ability to derive and apply novel insights from these and other emerging genomic technologies.

Familiarity with, and ability to critically evaluate, cutting-edge tools and frameworks for multi-omic data analysis, integration, visualization, and interpretation, and their application to clinical development.

Strong proficiency in at least one of R and Python, with a track record of writing reproducible and scalable code.

Proven experience in the application of state-of-the-art analytical, statistical and machine learning methods relevant to large scale analysis of biological data sets.

Strong interpersonal, matrix leadership and communication skills as well as ability to thrive in a matrix environment.

Proficient in both English and Chinese.

Preferred experience:

Demonstrated scientific capability as evidenced by publications, research reports, and external presentations.

Demonstrated delivery of high quality and state-of-the-art computational biology solutions and analyses in support of drug discovery projects.

Demonstrated experience in the analysis of multi-omic data linked to clinical phenotypes or measures for disease endotyping, biomarker discovery, mechanism and efficacy analysis and/or patient stratification.

Demonstrated experience in the analysis of multi-omic data from clinical studies and working in a regulatory environment.

Expertise in statistical approaches to the identification and assessment of predictive and prognostic biomarkers.

Experience in application of agentic tooling for computational delivery.