Principal Scientist driving cutting - edge GPCR structure - based drug discovery for chronic diseases in Shanghai. Collaborating within interdisciplinary teams using advanced computational methods.
About the role
- Principal Scientist/Senior Principal Scientist in Applied Genetics at GSK, applying human genetics expertise to develop new medicines and support therapeutic target hypotheses.
Responsibilities
- Apply expertise in human genetics, statistical genetics, genetic epidemiology, or a related discipline to support the development of new medicines.
- Contribute to the ongoing development and iteration of scaled and automated genetic assessment tools to support the advancing science of drug target identification and validation.
- Develop and/or critically evaluate new methods to derive and apply genetic insights to support the development of therapeutic target hypotheses.
- Perform and apply at-scale and bespoke computational and statistical analyses (e.g., GWAS, WGS, Mendelian Randomization, rare variant association testing, and variant to gene mapping) and implement new analytical methods to infer molecular mechanisms to elucidate and interrogate therapeutic hypotheses.
- Evaluate genetic and causal biology evidence to support therapeutic target hypotheses and provide clear and concise communication of results and interpretation to peers and leaders.
- Collaborate effectively in multi-disciplinary teams (internal and external) to answer complex scientific questions.
- Identify and implement creative solutions to address challenging scientific questions.
Requirements
- Ph.D in human genetics, statistical genetics, genetic epidemiology, or related disciplines with strong computational and quantitative focus
- Experience in development, critical evaluation and application of analyses methods to answer complex scientific questions
- Experience in performing analyses and the interpretation of findings of large-scale population based genetic studies (e.g., GWAS and WES/WGS approaches, MR and variant to gene mapping)
- Experience evaluating and integrating genetic and genomic data to evaluate strength of causal genetic evidence (e.g., QTL mapping & integration, causal inference, pathway enrichment)
- Proficient in programming/scripting in R or Python and working within cloud-based computing platforms (e.g., AWS and/or Google Cloud)
- Experience in working with genetics databases and resources including key biobanks and disease-specific consortia data sources
Benefits
- health care and other insurance benefits (for employee and family)
- retirement benefits
- paid holidays
- vacation
- paid caregiver/parental and medical leave
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