Associate Director, Computational Genomics and Informatics

Role Summary

Associate Director to lead the Computational Genomics and Informatics capability with hands-on technical contributions and strategic guidance. Focus on enabling analysis of genetic data from millions of individuals across multiple biobanks, in a hybrid role primarily located in Cambridge, MA.

Responsibilities

• Optimize internal infrastructure and capabilities for analyzing individual-level genetic data and performing meta-analysis across biobanks; manage partnerships with service providers to co-design analysis solutions.
• Build and implement genomics pipelines on external cloud-based platforms to perform GWAS and RVAS efficiently.
• Design and implement harmonization and meta-analysis solutions for GWAS and RVAS summary statistics; build a framework for searchable and accessible results.
• Monitor usage and costs to optimize resource utilization in internal and external computing environments.
• Collaborate with statistical geneticists, informatics group, and third-party providers; produce user-friendly documentation related to pipelines and meta-analysis.

Qualifications

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

Skills

• Genomics data analysis
• Cloud-based workflow implementation
• Data harmonization and meta-analysis
• Pipeline development and documentation
• Linux, Python, R

Education

• PhD in Bioinformatics, Computer Science, Statistical Genetics or related field