Sr. Scientist I, Translational Genetics & Real-World Evidence

The Position

The Senior Scientist will contribute to the integration of human genetics, multi-omics datasets, and real-world clinical data to generate human evidence supporting therapeutic development. This role will work at the intersection of translational genetics, computational biology, and real-world evidence (RWE) analytics to support target validation, patient stratification, clinical endpoint evaluation, and indication prioritization across Arrowhead therapeutic programs. The successful candidate will collaborate closely with the Translational Genetics & Data Science team as well as cross-functional partners including Real-World Evidence, Clinical Development, Translational Medicine, and Discovery Biology. The role will leverage large-scale biobank datasets, electronic health records (EHR), and other clinical datasets to translate human data into actionable insights supporting drug discovery and development. This position will play an important role in converting Arrowhead's growing access to biobank data and established computational pipelines into high-value insights that inform therapeutic strategy and precision medicine approaches.

This role is onsite 5 days a week in San Diego.

Responsibilities

• Integrate human genetics, multi-omics datasets, and real-world clinical data to generate evidence supporting therapeutic target validation and indication prioritization
• Analyze large-scale biobank and population datasets to identify genetic associations and disease mechanisms relevant to Arrowhead therapeutic programs
• Collaborate with the Real-World Evidence (RWE) team to analyze clinical datasets including electronic health records (EHR), claims databases, registries, and longitudinal cohort data
• Conduct statistical and computational analyses to characterize disease progression, patient heterogeneity, and treatment outcomes using real-world datasets
• Translate genetic findings and clinical observations into hypotheses that inform biomarker strategies, patient stratification, and clinical trial design
• Develop scalable analytical workflows to integrate genetics, omics, and clinical data sources
• Work collaboratively with cross-functional teams including Discovery Biology, Clinical Development, Translational Medicine, and Data Science to support program decisions
• Present results to internal stakeholders and contribute to internal reports, project discussions, and scientific publications
• Manage multiple projects simultaneously and communicate progress and findings effectively to project teams and leadership
• Adhere to data governance and compliance standards when working with sensitive clinical datasets

Requirements

• PhD in Biostatistics, Statistical Genetics, Epidemiology, Bioinformatics, Computational Biology, Biomedical Data Science, or a related quantitative discipline
• 3+ years of experience in human genetics, biomedical data science, or real-world data analytics
• Strong experience leveraging AI/LLMs to accelerate end-to-end research workflows, including coding, data analysis, literature synthesis, and report generation, with demonstrated improvements in efficiency and output quality.
• Experience working with biobank-scale datasets and real-world data sources such as EHR, claims databases, registries, or population cohorts
• Strong background in statistical genetics, epidemiology, or observational data analysis
• Experience integrating genetics, omics, and clinical datasets
• Proficiency in R or Python for large-scale data analysis
• Excellent problem-solving, analytical, and communication skills
• Ability to work effectively in a fast-paced, collaborative, and cross-functional research environment

Preferred

• Post-doc and industry experience
• Experience analyzing large-scale population genetics datasets such as UK Biobank, FinnGen, All of Us, or Genomics England
• Familiarity with analytical frameworks including phenome-wide association studies (PheWAS), Mendelian randomization, causal inference methods, and survival analysis
• Experience working with real-world clinical data models such as OMOP / OHDSI
• Experience collaborating with clinical development or real-world evidence teams
• Experience applying machine learning or advanced statistical methods to biomedical datasets
• Strong interest in translating human data into actionable insights for drug development