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.

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.

Supportive culture that values scientific excellence, diversity and inclusion, and sustainable work-life balance.

Senior / Principal Scientist, Statistical Genetics

Key skills