Associate Director, Statistical Genetics
About the role
The Human Genetics team at Alnylam is seeking a visionary scientific leader to head the Analytical Genetics function. This role is central to translating human genetic insights into life-changing RNAi therapeutics.
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.
Qualifications
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.
Pay
$166,200.00 - $249,200.00
Schedule
Onsite
Benefits
Comprehensive benefits including medical, dental, and vision coverage, life and disability insurance, a lifestyle reimbursement program, flexible spending and health savings accounts, and a 401(k) with a generous company match. Eligible employees enjoy paid time off, wellness days, holidays, and two company-wide recharge breaks. We also offer generous family resources and leave. Our commitment to your well-being reflects our belief that caring for our people fuels the impact we create together.