We're seeking a Statistical Genetics Scientist to assist with analysis for precision medicine programs targeting Parkinson’s disease risk and progression for GP2. You'll drive large GWAS efforts integrating *-omic data at biobank scale.

What You'll Do

Primary Responsibilities (70%)

• Design and execute large-scale GWAS for risk and progression phenotypes related to PD and related disorders
• Lead meta-analyses across heterogeneous cohorts with harmonized phenotypes
• Conduct statistical fine-mapping and credible set analysis at implicated loci
• Execute colocalization analyses (coloc, eCAVIAR) to identify shared causal variants across traits
• Run SMR/GSMR analyses integrating brain eQTL and pQTL data to prioritize causal genes
• Perform genetic correlation analyses (LDSC, HDL) between related phenotypes
• Build and deploy polygenic risk scores
• Design and execute bidirectional Mendelian Randomization analyses and MR sensitivity analyses

Secondary Responsibilities (30%)

• Integrate eQTL/pQTL data from brain tissues and relevant cell types
• Collaborate with bioinformatics team on multi-omic follow-up of GWAS signals and exploratory multi-omics
• Support network analyses by providing robustly prioritized gene lists

Required Qualifications

• MS or PhD in Statistical Genetics, Human Genetics, Genetic Epidemiology, Biostatistics, Bioinformatics or related quantitative field
• Published experience conducting and publishing GWAS (first-author publications demonstrating methodological rigor)
• Expert-level proficiency in GWAS pipelines (PLINK2, REGENIE, BOLT-LMM)
• Strong command of Mendelian Randomization methods and software (TwoSampleMR, MR-Base, MendelianRandomization R package)
• Experience with summary statistics-based methods (LD score regression, genetic correlation, colocalization)
• Proficiency in R and/or Python for statistical analysis and visualization
• Hands-on experience with cloud-based genomics platforms (e.g., Terra, Verily Workbench, UK Biobank Research Analysis Platform, DNAnexus, or equivalent)
• Strong statistical foundation (regression, mixed models, multiple testing, causal inference)
• Track record of working with biobank-scale datasets (100K+ samples)
• General best practices in epidemiology

Preferred Qualifications

• Experience with Parkinson’s, Alzheimer’s or neurodegenerative disease genetics
• Familiarity with longitudinal phenotype analysis and survival methods
• Experience integrating multi-ancestry GWAS data
• Knowledge of eQTL/pQTL resources (GTEx, MetaBrain, Brain xQTL, deCODE, UK Biobank pQTL)
• Publications in high-impact genetics journals

Technical Environment

You'll work in cloud-native genomics environments including Terra, Verily Workbench, and the UK Biobank RAP. Familiarity with WDL/Cromwell or Nextflow workflows is a plus. We leverage both CPU and GPU compute for large-scale analyses in the cloud.

To Apply

Submit CV and cover letter with links to 2-3 representative publications demonstrating GWAS methodology expertise as well as related GitHub content.

Compensation

$100,000 to $200,000 / year total compensation commensurate with experience. Multiple positions at various levels of seniority exist for this role.