Associate Manager, Statistical Genetics

A Typical Day Might Include The Following
- Perform genome-scale analyses with genotype, imputed, and sequence data from millions of individuals across hundreds of phenotypes and biomarkers.
- Build, maintain, and improve analytical workflows implementing analytical best practices.
- Build and manage systems to execute genomic analysis at large scale/high throughput, including analysis tracking and post-analysis quality control.
- Identify and troubleshoot genetic analyses at scale.
- Test and integrate computational tools for deployment across diverse data types (quantitative, health outcomes, and molecular).
- Critically review and provide input on analysis plans, results, and summaries to ensure accuracy/reliability; identify problems and propose solutions or analytical refinements.

This Role Might Be For You If You
- Have experience analyzing large genetic association studies and meta-analysis (e.g., UK Biobank or similar biobank-scale data).
- Have experience managing genetic/phenotype data, including sequence data handling (e.g., VCF), genotype imputation strategies, and QC of association inputs/outputs.
- Have demonstrated coding ability in Python, C/C++, or R.
- Communicate clearly to summarize/present methods and results to varied audiences.
- Are collaborative and motivated to identify therapeutic targets in cardiovascular, metabolic, and skeletal diseases.

To be considered
- PhD in Human Genetics, Biostatistics, or related field (postdoctoral or relevant industry experience preferred).
- Experience with genome-/exome-wide association, rare variant analysis, Mendelian randomization, LD Score regression, polygenic risk scoring, meta-analysis, and functional prioritization.
- Expertise with cloud computing, PLINK/REGENIE and advanced genomic analysis tools, plus R/Python/C/C++.
- Cardiovascular, metabolic, and/or musculoskeletal disease experience preferred.

Application
- Apply now.