Scientist, Statistical Genetics

Alnylam Pharmaceuticals • Cambridge, MA

250d • Hybrid

About The Position

The Research Department at Alnylam is seeking a passionate and creative scientist to join the Alnylam Human Genetics (AHG) team and play a leading role in applying human genetics in drug discovery and development. AHG has access to multiple large genetic datasets including UK Biobank, All of Us, Discover Me South Africa, and Our Future Health. Our team is focused on mining genetic data to make new discoveries relevant to drug development. This is an exciting opportunity to transform insights from human genetics into opportunities to help patients that have limited treatment options today. The successful candidate will be part of a team conducting in-depth analyses of human genetic data to 1) fuel our efforts to identify novel drug targets in important disease areas and 2) gain biological insights into targets and drugs we are already pursuing that will enhance the development path of our medicines and enable us to reach more patients. We are open to onsite or hybrid working in Cambridge, USA.

Requirements

• PhD in statistical genetics or related field with experience in mining large population cohort datasets to discover sequence variants associated with disease.
• Demonstrated expertise in managing and analyzing large GWAS and/or RVAS with a track record of making novel biological discoveries using these data.
• Knowledge of statistical packages commonly used for GWAS and RVAS (e.g., PLINK, REGENIE, SKAT, etc.).
• Advanced hands-on knowledge of at least one programming language such as R or Python.
• Experience using cloud computational environments for genomics such as the All of Us Researcher Workbench or those provided by DNA Nexus (e.g., UKB RAP).
• Experience conducting meta-analysis, colocalization, and Mendelian randomization would be an advantage.
• Ability to work independently, excellent communication skills, and a track record of publishing in high impact scientific journals.

Responsibilities

• Run GWAS and rare variant association studies (RVAS) at biobank-scale, perform meta-analysis across biobanks, run and interpret post-GWAS analyses (e.g. colocalization with molecular QTLs).
• By integrating these results and complementary biological data, independently identify novel drug targets for RNAi therapeutics.
• Identify and implement the latest statistical and analytical methods to help us make discoveries.
• Work closely with project teams to create focused genetic analyses to interrogate individual targets we are either actively pursuing or considering.
• Use these analyses to assess genetic support for safety and efficacy of these targets and define patient populations and unmet medical needs and expand indications.
• Develop expertise in genetics relevant to biological areas of interest to Alnylam (e.g. metabolic disease, cardiovascular disease, rare disease).
• Independently manage workload including balancing large-scale projects with time-sensitive requests.
• Prepare, review, and deliver high quality scientific manuscripts and presentations for internal/external use.

What This Job Offers

• Industry: Chemical Manufacturing
• Education Level: Ph.D. or professional degree