Associate Director, Statistical Genetics

Alnylam Pharmaceuticals•Cambridge, MA

4d•$166,200 - $249,200•Onsite

About The Position

Alnylam’s Human Genetics team is seeking a bold and visionary scientific leader to head our Analytical Genetics function. This is a high-impact role at the center of our efforts to translate human genetic insights into life-changing RNAi therapeutics. You will lead the application of cutting-edge analytical approaches to some of the largest and most informative human genetic datasets in the world—spanning millions of individuals across global resources such as UK Biobank, All of Us, Our Future Health, FinnGen, Precision Health Research Singapore, and the Alliance for Genomic Discovery. You will shape the future of genetics-driven drug discovery at Alnylam: defining strategy, advancing innovative methods, and building scalable approaches that accelerate therapeutic hypothesis generation. This role offers a unique opportunity to directly influence how human genetics informs target discovery and to help deliver transformative medicines to patients worldwide. This position is onsite and will be primarily located in Cambridge, MA.

Requirements

PhD in Statistical Genetics or a related field with 8+ years of relevant postdoctoral or industry experience.
Demonstrated experience managing and developing teams to deliver high-impact results.
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, phenotypic, and multi-omic data (e.g., proteomics), with a track record of 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.

Responsibilities

Lead and grow a high-performing Analytical Genetics group, establishing scientific direction, prioritizing impactful work, and building capabilities to deliver genetics-driven discovery at scale.
Design and oversee large-scale analyses of common and rare genetic variation across biobank-scale datasets, contributing to critical analyses while implementing robust, scalable frameworks and standards.
Define and execute cross-biobank analysis strategies, developing approaches that maximize discovery power across diverse internal and external datasets.
Guide post-GWAS interpretation and causal inference efforts (e.g., fine-mapping, colocalization, Mendelian randomization) to enable confident causal gene nomination and therapeutic hypothesis generation.
Identify and implement emerging analytical approaches (e.g., multi-omics integration, polygenic modeling, rare variant aggregation, and AI/ML-enabled prioritization) to continuously advance methodological capabilities.
Develop and mentor scientific talent, fostering technical excellence, leadership growth, and a culture focused on rigor, innovation, and impact.
Represent Alnylam externally through high-impact publications, conference presentations, and strategic scientific engagements.
Evaluate and shape external data partnerships and new resources to expand discovery capabilities and maintain leadership in genetics-driven drug development.