Associate Manager, Statistical Genetics

Role Summary

Associate Manager, Statistical Genetics. We are seeking a statistical geneticist to contribute to the analysis and interpretation of genotyped and sequenced human data linked with rich phenotype information to generate insights into disease mechanisms and support the development of medicines. The role involves collaborating with genomic data analysis, translational genetics, functional biology, and medicine teams to design, execute, and refine analyses that connect genetic variation to health and disease, and to develop statistical and computational tools for scalable analyses.

Responsibilities

• Perform genome-scale analyses with genotype, imputed, and sequence data from millions of individuals across hundreds of relevant phenotypes and biomarkers.
• Maintain and improve analytical workflows across teams and implement analytical best practices.
• Integrate computational tools and diverse molecular data types to generate insights about human disease, interpret results, and prioritize candidate therapeutic targets.
• Critically review analysis plans, results, and summaries to ensure accuracy and reliability; identify problems and propose solutions or refinements.

Qualifications

• PhD in Human Genetics, Biostatistics, or a related field with 0‚Äì2 years of experience (postdoctoral training or relevant industry experience preferred).
• Experience in genome- and exome-wide association analyses, rare variant analysis, Mendelian randomization, LD Score regression, polygenic risk score modelling, meta-analysis, and use of functional data to prioritize variants and genes of interest.

Skills

• Experience analyzing large genetic association studies and meta-analysis, including UK Biobank or other biobank-scale data.
• Experience managing genetic and phenotype data, familiarity with sequence data (e.g., VCF), genotype imputation, and QC for genetic association inputs and outputs.
• Proficiency in Python, C/C++, or R.
• Strong communication skills to summarize and present concepts, methods, and results to diverse audiences.

Education

• PhD in Human Genetics, Biostatistics, or related field required.

Additional Requirements

• Experience with cloud computing environments and tools for genomic analyses (e.g., PLINK, REGENIE).
• Experience in cardiovascular, metabolic, and/or musculoskeletal diseases preferred.