Principal Scientist, Genetic Epidemiology & Biostatistics

Lead with hypothesis-driven thinking and demonstrate an ability to put questions in a translational/clinical context. Strong desire to perform individual contributor work in a collaborative context and communicate findings to wet lab scientists and clinicians. Guide preclinical strategy using biobanks, electronic health record, natural history studies, and clinical trial data, applying epidemiological principles. Read the literature, derive own insights, and identify novel data and analyses methods to answer questions in therapeutic development with data. Identify and prioritize causal disease drivers from observational data (genetics, omics, longitudinal). Apply and explain epidemiological and study design approaches used to arrive at conclusions to diverse audiences. Collaborate with human genetics to implement and maintain a scalable framework for generating robust and novel insights from human genetics data. This framework will handle the storage of phenotype information, facilitate genotype-to-phenotype analyses, and enable the integration of external/publicly available summary level data. Gain increasing independence and responsibility. Will be expected to manage workload including balancing large-scale projects with time-sensitive requests. Strong written and oral presentation skills, ability to draft high quality scientific manuscripts and presentations for internal/external use. PhD in epidemiology, biostatistics, human genetics, or related field with substantial hands-on computational experience. Proven track record of working with data, sharing code, constructing pipelines, and showcasing results in either manuscripts, conferences, or similar. Proficiency in python and/or R, familiarity with basic computational and cloud infrastructures and databases. Experience with the UKB Research Analysis Platform, AllOfUs Researcher Workbench, and/or similar environments is a plus. Experience with implementing and interpreting summary statistic-based analyses including but not limited to colocalization and Mendelian Randomization, and select which methods are the most practical or applicable for the questions at hand.