Associate Director, Statistical Genetics

Role Summary

Associate Director to lead large-scale statistical genetics capability with hands-on technical contributions and strategic guidance. This role supports analysis of genetic data from millions of individuals across multiple biobanks, including UK Biobank, All of Us, Discover Me South Africa, Our Future Health, and the Alliance for Genomic Discovery. Hybrid position, primarily located in Cambridge, MA.

Responsibilities

• Perform common and rare genetic association studies using biobank-scale data to identify new targets for RNAi therapeutics.
• Conduct cross-biobank meta-analyses, including leveraging publicly available summary statistics.
• Organize results to facilitate use by the broader team.
• Perform post-GWAS analyses to identify 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.
• 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.

Qualifications

• PhD in Statistical Genetics or 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 GWAS and RVAS methods and experience implementing relevant statistical packages (e.g., REGENIE, PLINK).
• Extensive experience processing and analyzing biobank-scale genetic, phenotypic, and multi-omic data, with a track record of novel discoveries.
• Proven experience performing multi-biobank analyses and meta-analyses, with understanding of meta-analytic approaches for handling sample overlap.
• Experience applying variant-to-gene post-GWAS methods (statistical fine-mapping, colocalization, Mendelian randomization).
• Experience with multivariate phenotype analysis, time-to-event and longitudinal analysis, and diverse ancestral datasets.
• Hands-on experience QC-ing biobank-scale genetic data (WGS, WES, imputed).
• Expertise in phenotype generation and cross-biobank phenotype curation and harmonization.
• Experience with Linux command line and Python and R.
• Experience implementing genomics workflows on cloud platforms (DNAnexus, All of Us Researcher Workbench, Terra).
• Excellent communication, collaborative and cross-functional skills, with a track record of publishing in high-impact journals.

Skills

• Statistical genetics
• Biobank-scale data analysis
• GWAS and RVAS methods
• Meta-analysis and cross-biobank integration
• Post-GWAS methods (fine-mapping, colocalization, Mendelian randomization)
• Multivariate and longitudinal analysis
• Python, R, Linux
• Cloud-based genomics workflows
• Scientific communication and manuscript preparation