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Statistical Geneticist with Machine Learning (Drug Discovery)
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We are partnered with a cutting-edge Biotech who are researching and modelling health resilience, unlocking novel therapies. Combining large-scale human genetics, multi-omics, and AI, our client are driving target discovery and accelerating drug development in partnership with leading pharma companies.
They are hiring a Statistical Geneticist with Machine Learning experience to contribute to their genetics-driven causal AI platform:
Responsibilities
- Lead GWAS, PheWAS, PRS, rare variant and post-GWAS analyses
- Integrate multi-omics QTL data (eQTL, pQTL, mQTL) for gene prioritisation and causal inference
- Apply ML-derived and continuous phenotypes to enhance genetic discovery
- Build scalable, reproducible pipelines for population-scale datasets
- Work cross-functionally with ML, biology, and engineering teams to drive drug discovery decisions
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Requirements
- MSc or PhD in Statistical Genetics, Bioinformatics, Biostatistics, Computational Bio or similar.
- Clear evidence of GWAS ownership, Mendelian Randomization, colocalisation and post-GWAS interpretation in career to date.
- First authorships on published papers related to ML and/or StatGen
- Demonstrated skills & experience in Python/PyTorch, R and Unix/Linux
- Experience with HPC or cloud computing
- Experience in deep learning or ML applied to GWAS/Genetic pipelines
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