About The Position
We are seeking a Statistical Geneticist with expertise in whole genome sequencing (WGS), proteomics, and clinical outcomes analysis to advance our research in identifying novel therapeutic targets. This role will involve analyzing large-scale biobank and population-cohort datasets to uncover genetic and molecular factors associated with disease risk, progression, and treatment response. You will operate as part of the CMR Data Science and Computational Biology (DSCB) team and partner with early discovery scientists and clinicians, translational scientists, bioinformaticians and geneticists.
Requirements
- PhD in statistical genetics, bioinformatics, computational biology, biostatistics, or a related quantitative field
- Qualified applicants must be authorized to work in the United States on a full-time basis.
Nice To Haves
- Expertise in whole genome and whole exome sequencing analysis, proteomics, metabolomics and other molecular data analysis, and clinical outcomes research.
- Strong proficiency in statistical modeling, machine learning, and high-dimensional data analysis.
- Experience working with large biobank and cohort datasets (e.g., UK Biobank, All of Us, FinnGen).
- Proficiency in programming languages such as R, Python, and SQL for data analysis.
- Familiarity with genetic association studies, GWAS, and polygenic risk scores.
- Excellent communication and collaboration skills to work effectively in cross-functional teams.
- Experience in pharmaceutical or biotech industry settings.
- Knowledge of functional genomics and multi-omics data integration.
- Strong publication record demonstrating contributions to statistical genetics and biomarker discovery and analysis.
- Prior experience in cardiometabolic research.
- Prior experience with polygenic risk score models.
Responsibilities
- Apply statistical and computational approaches to analyze WGS/WES, proteomics, metabolomics, and clinical data for biomarker discovery.
- Conduct rigorous analyses of large-scale population cohorts and biobank datasets to identify genetic variants and causal genes associated with disease outcomes.
- Develop and implement machine learning and bioinformatics pipelines to integrate multi-omics data.
- Collaborate with interdisciplinary teams, including geneticists, epidemiologists, and clinicians, to interpret findings and guide therapeutic development.
- Prepare scientific reports, presentations, and publications detailing research outcomes.
- Contribute to the development of novel statistical methods for analyzing high-dimensional biological data.
Benefits
- Attractive, market-leading salary package.
- Clear career advancement path with professional development opportunities.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
Ph.D. or professional degree
