Scientist, Statistical Genetics

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 1) assess genetic support for safety and efficacy of these targets and 2) 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. PhD in statistical genetics or related field with experience in mining large population cohort datasets to discover sequence variants associated with disease. Title is commensurate with experience. 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.