Senior Scientist, Statistical Genetics
Alnylam Pharmaceuticals
August 30, 2026
On-site
Cambridge, MA
Clinical Research and Development
Key Responsibilities
- Perform common and rare variant association studies (including large-scale all-by-all analyses) using biobank-scale data to identify and prioritize novel targets for RNAi therapeutics.
- Lead cross-biobank meta-analyses integrating internal and publicly available summary statistics to maximize power, replication, and trans-ancestry discovery.
- Implement post-GWAS methods to nominate causal genes and therapeutic hypotheses, including fine-mapping, colocalization, Mendelian randomization, and functional annotation integration.
- Drive methodological innovation (e.g., multi-omics integration, polygenic modeling, rare variant aggregation, AI/ML-enabled prioritization).
- Establish best practices for data organization, knowledge management, and results dissemination.
- Enhance external visibility via high-impact publications, conference presentations, and strategic collaborations.
Qualifications
- PhD in Statistical Genetics (or related) with 3+ years relevant postdoctoral/industry experience.
- Expertise in statistical genetics (GWAS/RVAS; single-variant testing, burden tests, SKAT) and experience with REGENIE, PLINK.
- Experience analyzing individual-level, biobank-scale genetic and phenotypic data with a record of novel discoveries.
- Experience with multi-biobank analyses/meta-analyses (METAL, RAREMETAL, REMETA), including sample overlap handling.
- Variant-to-gene post-GWAS expertise (fine-mapping, colocalization, Mendelian randomization).
- Experience with multi-ancestry data; scalable tools for multivariate, time-to-event, and longitudinal analyses.
- Hands-on QC on biobank-scale WGS/WES/imputed data; phenotype generation and cross-biobank curation/harmonization.
- Proficiency in Linux; hands-on Python and R.
- Cloud genomics workflows (DNAnexus, All of Us Researcher Workbench, Terra).
- Strong communication/collaboration skills and high-impact publication record.
- Perform common and rare variant association studies (including large-scale all-by-all analyses) using biobank-scale data to identify and prioritize novel targets for RNAi therapeutics.
- Lead cross-biobank meta-analyses integrating internal and publicly available summary statistics to maximize power, replication, and trans-ancestry discovery.
- Implement post-GWAS methods to nominate causal genes and therapeutic hypotheses, including fine-mapping, colocalization, Mendelian randomization, and functional annotation integration.
- Drive methodological innovation (e.g., multi-omics integration, polygenic modeling, rare variant aggregation, AI/ML-enabled prioritization).
- Establish best practices for data organization, knowledge management, and results dissemination.
- Enhance external visibility via high-impact publications, conference presentations, and strategic collaborations.
Qualifications
- PhD in Statistical Genetics (or related) with 3+ years relevant postdoctoral/industry experience.
- Expertise in statistical genetics (GWAS/RVAS; single-variant testing, burden tests, SKAT) and experience with REGENIE, PLINK.
- Experience analyzing individual-level, biobank-scale genetic and phenotypic data with a record of novel discoveries.
- Experience with multi-biobank analyses/meta-analyses (METAL, RAREMETAL, REMETA), including sample overlap handling.
- Variant-to-gene post-GWAS expertise (fine-mapping, colocalization, Mendelian randomization).
- Experience with multi-ancestry data; scalable tools for multivariate, time-to-event, and longitudinal analyses.
- Hands-on QC on biobank-scale WGS/WES/imputed data; phenotype generation and cross-biobank curation/harmonization.
- Proficiency in Linux; hands-on Python and R.
- Cloud genomics workflows (DNAnexus, All of Us Researcher Workbench, Terra).
- Strong communication/collaboration skills and high-impact publication record.