Associate Director, Statistical Genetics

Perform common and rare genetic association studies, including large “all by all” analyses, using biobank-scale data (e.g., UK Biobank, All of Us, Our Future Health, Alliance for Genomic Discovery) with the aim of finding new targets for RNAi therapeutics. Conduct cross-biobank meta-analyses, including leveraging publicly available summary statistics. Organize these results in a manner that facilitates their use by the broader team. Perform post-GWAS analyses aimed at identifying causal genes and potential therapeutic targets (e.g., fine-mapping, colocalization, Mendelian randomization) Identify, evaluate and implement the latest statistical genetics innovations and analytical methods to help us make discoveries. Manage, coach and develop a small team focused on biobank-scale analyses, ensuring scientific rigor and timely delivery of results. Prepare, review, and deliver high quality scientific manuscripts and presentations for internal and external use. PhD in Statistical Genetics or a related field with 8+ years of relevant post-graduate experience. Proven track record of managing people and driving teams to produce results. Deep understanding of statistical genetics including GWAS and RVAS methods (e.g., single variant testing, burden, SKAT) and extensive experience implementing relevant statistical packages (e.g., REGENIE, PLINK). Extensive experience processing and analyzing individual-level biobank-scale genetic, phenotypic, and multi-omic data (e.g., proteomics), and a track record of making novel discoveries using these data. Proven track record of performing multi-biobank analyses, including performing meta-analyses (e.g., using METAL, RAREMETAL, REMETA), and understanding of meta-analytic approaches for handling sample overlap . Demonstrated experience in applying variant-to-gene post-GWAS methods (statistical fine-mapping, colocalization, Mendelian randomization). Experience with statistical genetics approaches and scalable tools for multivariate phenotype analysis, time-to-event and longitudinal analysis, and leveraging genetic ancestrally diverse datasets to improve signal detection and resolution. Hands-on experience conducting processing and QC of biobank-scale individual-level genetic data (WGS, WES, imputed). Expertise in phenotype generation as well as cross-biobank phenotype curation and harmomonization. Experience working on a Linux command line and advanced hands-on knowledge of Python and R. Practical experience implementing genomics workflows on cloud-based platforms such as DNAnexus, All of Us Researcher Workbench, Terra. Excellent communication skills, an ability to work collaboratively and cross-functionally, and a track record of publishing in high impact scientific journals.