Associate Director, Computational Genomics and Informatics

Overview

The Alnylam Human Genetics (AHG) team is looking for an Associate Director to lead our Computational Genomics and Informatics capability through direct, hands-on technical contributions and strategic guidance. The successful candidate will help enable 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.

This position is hybrid and will be primarily located in Cambridge, MA.

• Optimize our internal infrastructure and capabilities for analyzing individual-level genetic data and performing meta-analysis across biobanks. This includes managing partnerships with service providers to co-design analysis solutions.
• Build and implement genomics pipelines on external cloud-based platforms (e.g. DNA Nexus, All of Us Researcher workbench) to allow us to efficiently perform GWAS and RVAS.
• Design and implement solutions for harmonization and meta-analysis of GWAS and RVAS summary statistics, and efficient post-GWAS analysis. Build a framework to ensure these results are easily searchable and accessible.
• Monitor usage and costs to optimize resource utilization in both internal and external computing environments.

This role will involve close collaboration with our statistical geneticists, informatics group, and third-party service providers. The successful candidate will produce user-friendly documentation related to running pipelines, meta-analysis, etc.

• PhD in Bioinformatics, Computer Science, Statistical Genetics or a related field with 8+ years of relevant experience in Computational Biology or a similar field. Title is commensurate with experience.
• Deep understanding of statistical genetics including GWAS and RVAS methods and extensive experience implementing statistical packages used for GWAS (e.g., Regenie, PLINK).
• Extensive experience working with biobank-scale genetic and phenotypic data.
• Proven track record implementing genomics workflows on cloud-based platforms such as DNA Nexus, All of Us Researcher Workbench.
• Hands-on experience building portable pipelines across cloud environments using workflow languages (e.g., CWL, WDL, Nextflow) and software containerization.
• Experience with processing and QC of individual-level genetic data (WGS, WES, imputed). Experience with tools for genetic data processing (e.g., VCFtools) and variant annotation (VEP/WGSA).
• Experience working on a Linux command line and advanced hands-on knowledge of Python and R.
• Familiarity with variant-to-gene post-GWAS methods (statistical fine mapping, colocalization, Mendelian Randomization).
• Proficiency in implementing web-based tools for genomics (dashboards, Shiny applications, Phe Web) would be an advantage.