锦鲤求职岗位描述
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 an innovative and experienced scientist to join our team as a Statistical geneticist. Within GSK R&D Translational Sciences, the Genomic Technologies team integrates deep and broad expertise in computational and statistical methods and techniques to enable best-in-class analytical insight and interpretation of genetics and genomics data, impacting GSK's R&D portfolio and pipeline decisions. Your work will have application across our pipeline, from discovery of new causal gene-phenotype links to biomarker identification and patient stratification, with a focus on large-scale human genetic and genomic data.
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 and curate large-scale genetic and genomic studies, including whole-exome and genome sequencing.
Design and implement rigorous quality control workflows covering sample-level and variant-level metrics.
Conduct biobank-scale genotype-phenotype associations.
Implement survival and time-to-event analyses linking genetic variants and protein levels to disease incidence, mortality, and EHR-derived endpoints.
Conduct proteome-wide association studies and protein–disease hazard modelling.
Work across multiple secure computing platforms and data repositories.
Produce high-quality summary statistics and visualisations suitable for internal review and publication.
Maintain versioned, auditable pipelines and detailed documentation to support reproducibility and regulatory compliance.
Basic qualifications:
Advanced degree (PhD or equivalent) in a relevant scientific discipline.
Experience with genetic and genomic data, and with state-of-the-art tools for data processing, analysis and interpretation (e.g. bcftools, PLINK2, GATK, REGENIE).
Excellent programming skills in R and/or Python, and application of techniques in reproducible research, literate programming, FAIR data principles, or software development.
Experience of evaluating, improving, testing, and/or developing methods for analysis and interpretation of large-scale genetics or genomics data.
Strong team-working and English communication skills.
Preferred experience:
Experience with machine learning and/or advanced statistical methods.
Experience working in multidisciplinary teams on complex and impactful projects.
Experience with integrating information across multiple genetic, genomic, epigenomic, or experimental/functional genomic data types.
Experience with analysis of very large datasets using distributed or cloud computing technologies (e.g. SQL, PySpark, BigQuery, Docker, Nextflow).