Scientific Leader, Statistical Genetics

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 Statistical Geneticist / Genetics Analyst to join the Genomic Technologies capability within Asia Translational Medicine, based in Shanghai, China.

The role will support the design, execution, quality control, interpretation and documentation of genetic and genomic analyses across large-scale human genetics, biobank, sequencing and multi-omic datasets. The work will inform target discovery, causal mechanism evaluation, biomarker development, patient stratification and translational decision making across the R&D portfolio.

Principal accountabilities will focus on delivery for Asia programs, with alignment to global R&D Translational Sciences and Genomic Technologies teams to ensure analytical consistency, scientific robustness and reproducibility.

In this role, you will partner closely with colleagues across R&D Translational Sciences to design and deliver rigorous, scientifically robust genetic and genomic analyses that inform key portfolio and pipeline decisions. You will bring deep expertise in the analysis of large-scale biobank data to deliver insights that impact target discovery, causal mechanism identification, and biomarker development.

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 and may have the opportunity to spend time in our European and US hubs to develop and deliver that global strategy.

We value collaborative working and scientific evaluation of different approaches to improving our capabilities in areas of focus for Genomic Technologies, including discovery of new causal gene-phenotype links by advancing methodology for analysis of large-scale biobank individual human genetic data and of GWAS/xQTL summary statistics; identification of causal mechanisms, candidate biomarkers, disease subtypes and patient subgroups by analysing and integrating genetic, multi-'omic, and phenotypic data from human populations and from genome-scale gene editing perturbation experiments; and predictive modelling of the presence, direction and magnitude of causal gene-phenotype links, using genetic and 'omic data, mechanistic annotation, inferred causal networks, and genome-scale perturbation data.

Key responsibilities:

Process, curate and quality-control large-scale genetic and genomic datasets, including genotype array, whole-exome sequencing, whole-genome sequencing and linked phenotype data.

Implement sample-level and variant-level quality-control workflows using established tools and best practices, ensuring traceability and reproducibility of analytical outputs.

Conduct genotype-phenotype association analyses, including common and rare variant approaches, under the guidance of senior genetics and translational science colleagues.

Support survival and time-to-event analyses linking genetic variants, molecular traits, biomarkers and clinical or real-world endpoints.

Contribute to proteome-wide, transcriptome-wide and multi-omic association analyses where human genetics evidence is used to support disease mechanisms and biomarker hypotheses.

Work across approved secure computing environments and data repositories, complying with data governance, privacy and information security requirements.

Develop, maintain and document reproducible analytical pipelines in R and/or Python, using version control and auditable workflow practices.

Produce clear analysis summaries, visualisations and interpretation-ready outputs for project teams, governance discussions, internal review and potential publication.

Partner with Translational Omics, CPMS, Clinical Biomarker, Predictive Science, BBP, Clinical Development and other cross-functional partners to connect genetics insights with the broader translational evidence-generation loop.

Contribute to continuous improvement of analytical standards, methods evaluation and ways of working for genetics and omics delivery in Asia.

Basic qualifications:

Advanced degree, preferably PhD or equivalent experience, in statistical genetics, genetic epidemiology, bioinformatics, computational biology, biostatistics, data science or a related quantitative discipline.

Hands-on experience analysing genetic or genomic data, with familiarity in state-of-the-art tools such as PLINK/PLINK2, bcftools, GATK, REGENIE, SAIGE or equivalent tools.

Strong programming capability in R and/or Python, with experience writing reproducible, documented and scalable analytical code.

Understanding of GWAS, sequencing analysis, genotype imputation, variant annotation, association testing and genetic quality control principles.

Ability to work with complex phenotype and clinical data, and to collaborate with multidisciplinary scientific and clinical teams.

Strong written and verbal communication skills in English; Chinese language capability is preferred for local stakeholder engagement.

Preferred experience:

Experience with large-scale biobank or population-scale datasets and secure data analysis environments.

Experience integrating genetic, genomic, proteomic, transcriptomic, phenotypic or clinical data to support translational hypotheses.

Experience with survival modelling, time-to-event analysis, causal inference, Mendelian randomisation, colocalisation, fine-mapping or polygenic risk modelling.

Experience with workflow tools, containers or cloud/distributed compute, such as Nextflow, Docker, SQL, PySpark or cloud analytics environments.

Experience working in pharmaceutical R&D, clinical research, translational science or regulated data environments.