Senior Scientist, Statistical Genetics

Senior Scientist, Statistical Genetics

The Alnylam Human Genetics (AHG) team is seeking an exceptional statistical geneticist to join our Analytical Genetics group. The successful candidate will apply cutting-edge methods to analyze genetic data from millions of individuals, driving discoveries that directly inform our RNAi therapeutics pipeline. In this role, you will champion methodological innovation and build scalable approaches that unlock insights from some of the world's richest genomic resources, including UK Biobank, All of Us, Our Future Health, Finn Gen, Precision Health Research Singapore, the Alliance for Genomic Discovery and Helix.

This is a unique opportunity to translate large-scale human genetics into therapeutic targets and ultimately deliver transformative medicines to patients. This position is onsite and will be primarily located in Cambridge, MA.

Key Responsibilities:

• Perform common and rare variant association studies, including large-scale "all-by-all" analyses, using biobank-scale data (e.g., UK Biobank, All of Us, Our Future Health, Alliance for Genomic Discovery, Helix) 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 by identifying, evaluating, and deploying emerging approaches (e.g., multi-omics integration, polygenic modeling, rare variant aggregation, AI/ML-enabled prioritization).
• Help to establish best practices for data organization, knowledge management, and results dissemination.
• Enhance external visibility through high-impact publications, conference presentations, and strategic collaborations.

Qualifications:

• PhD in Statistical Genetics or a related field with 3+ years of relevant postdoctoral or industry experience.
• Deep expertise in statistical genetics, including GWAS and RVAS methods (e.g., single-variant testing, burden tests, SKAT), and extensive experience with relevant software (e.g., REGENIE, PLINK).
• Extensive experience processing and analyzing individual-level, biobank-scale genetic, and phenotypic data, with a track record of making novel discoveries.
• Proven experience conducting multi-biobank analyses and meta-analyses (e.g., using METAL, RAREMETAL, REMETA), including methods to account for sample overlap.
• Demonstrated expertise in variant-to-gene post-GWAS approaches (e.g., statistical fine-mapping, colocalization, Mendelian randomization).
• Experience applying scalable statistical genetics tools to multivariate phenotype analysis, time-to-event and longitudinal analyses, and genetically diverse datasets to improve signal detection and resolution.
• Hands-on experience processing and performing quality control (QC) on biobank-scale individual-level genetic data (WGS, WES, imputed data).
• Expertise in phenotype generation and cross-biobank phenotype curation and harmonization.
• Proficiency with Linux environments and advanced hands-on experience in Python and R.
• Practical experience implementing genomics workflows on cloud-based platforms (e.g., DNAnexus, All of Us Researcher Workbench, Terra).
• Excellent communication skills, ability to work collaboratively and cross-functionally, and a strong publication record in high-impact scientific journals.

U.S. Pay Range $ - $