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About the role
- Senior Scientist, Statistical Genetics at Novo Nordisk Research Center China. Leveraging human genetic evidence to inform drug discovery and portfolio decisions in a collaborative research environment.
Responsibilities
- Co-Define and lead local statistical genetics strategy for early discovery and translational projects
- Design and execute population and statistical genetics analyses, leveraging biobanks, cohorts, and multi-ancestry datasets
- Integrate human genetics with patient-derived multi-omics, functional assays, and disease models to generate mechanistic hypotheses and decision relevant insights.
- Embed genetic evidence into target prioritisation and portfolio decision frameworks, clearly articulating strengths, limitations, and uncertainty.
- Build and scale local capability in human genetics (and potentially RWE), including methods, workflows, and scientific best practices, in alignment with global strategies.
- Partner closely with digital biology, target discovery and progression teams to guide target discovery program ideation to data package maturation towards pipeline project approval.
- Communicate complex scientific findings clearly and confidently to senior stakeholders, translating analyses into strategic recommendations.
- Contribute to scientific publications, internal knowledge sharing, and external visibility, strengthening NNRCC’s impact in the local and global ecosystem.
Requirements
- PhD in human genetics, statistical genetics, genomics, computational biology, bioinformatics, epidemiology, or a closely related field.
- Significant post-PhD experience in pharmaceutical, biotech, or translational research environments, with demonstrated impact on discovery or early development programmes.
- Knowledge and experience in GWAS, WGS/WES, Mendelian Randomization, fine mapping, rare variant analysis, colocalization, QTL integration and polygenic risk.
- Deep expertise in human genetics and population genomics, including experience with large scale human genetic datasets and post GWAS analyses.
- Understanding of how genetic evidence is used to de-risk and prioritise drug targets across the discovery value chain is highly preferred.
- Proficiency in relevant analytical and programming environments (e.g. Python, R), with experience working on complex, high dimensional biological data.
- Experience enabling secure, reproducible, and compliant data workflows while supporting efficient local scientific execution and portfolio delivery.
- Ability to operate independently at a senior level, driving initiatives end-to-end and influencing cross-functional teams without formal authority.
- Strong communication skills and a proven ability to engage and collaborate across disciplines and cultures.
- Fluent in English is a MUST.
Benefits
- Supportive culture that values scientific excellence, diversity and inclusion, and sustainable work-life balance.
Job title
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