Overview In this role you will advance human genetics and real-world data to support therapeutic development at Arrowhead. You will work at the intersection of translational genetics, computational biology, and real-world evidence to inform target validation, patient stratification, and clinical trial design. You'll analyze large biobanks, EHRs, and other clinical datasets to turn human data into actionable insights that guide program decisions. Collaborating with cross-functional teams, you will shape precision medicine strategies and contribute to high-impact publications. This on-site role in San Diego offers the opportunity to influence drug discovery in a fast-paced biotech environment. ResponsibilitiesIntegrate genetics, multi-omics, and real-world data to support target validation and indication prioritizationAnalyze large-scale biobank and population datasets to identify genetic associations and disease mechanismsCollaborate with Real-World Evidence (RWE) team to analyze EHRs, claims, registries, and longitudinal dataPerform statistical and computational analyses to characterize disease progression and treatment outcomes in real-world dataTranslate genetic findings and clinical observations into hypotheses for biomarker strategies and patient stratificationDevelop scalable analytical workflows to integrate diverse data sourcesCollaborate with Discovery Biology, Clinical Development, Translational Medicine, and Data Science to support program decisionsPresent results to internal stakeholders and contribute to reports and publicationsManage multiple projects and communicate progress to teams and leadershipAdhere to data governance and compliance when handling sensitive clinical datasets Key requirementsPhD in a quantitative discipline3+ years of experience in human genetics, biomedical data science, or real-world data analyticsStrong experience leveraging AI/LLMs to accelerate research workflowsExperience with biobank-scale datasets and real-world data sources (EHR, claims, registries, cohorts)Background in statistical genetics, epidemiology, or observational data analysisExperience integrating genetics, omics, and clinical datasetsProficiency in R or Python for large-scale data analysisExcellent problem-solving, analytical, and communication skillsAbility to work in a fast-paced, collaborative environmentstrong communicationcollaboration across cross-functional teamsproblem-solving orientationRPythonstatistical genetics
Not specified in the original listing.
Not specified in the original listing.
