Summer Intern, Statistical Genetics & Human Biobanks

The Position

The Statistical Genetics & Human Biobanks Intern will support Translational Genetics research at Arrowhead Pharmaceuticals by developing AI-augmented workflows that integrate large-scale global human biobank data to identify disease-relevant genes for RNAi target discovery. This internship offers hands-on experience applying statistical genetics, functional genomics, and causal inference methods to real-world therapeutic discovery challenges. The intern will collaborate closely with computational and translational scientists to generate high-confidence, genetically supported hypotheses that inform target prioritization and decision-making.

This is an 11-week Summer Internship Program paying $27.00 per hour and this role requires full-time, onsite work five days per week at the designated location.

Responsibilities

* Support analysis of large-scale genetic and biomedical datasets to help identify disease-relevant signals.

* Assist in developing and applying analytical and AI-enabled workflows for data integration, interpretation, and reporting.

* Contribute to evidence synthesis and data summarization to support research and target evaluation efforts.

* Help assess consistency and robustness of findings across multiple datasets or sources.

* Prepare clear summaries, visualizations, or reports to communicate results to the project team.

* Present updates and findings during team meetings and participate in scientific discussions.

* Collaborate with scientists and computational team members on research projects and special initiatives.

Requirements

* Currently pursuing a PhD in Statistical Genetics, Human Genetics, Computational Biology, Bioinformatics, Biostatistics, or a closely related field.

* Hands-on experience analyzing data from at least one major human biobank.

* Proficiency in Python and/or R for statistical genetics and data analysis workflows.

* Familiarity with GWAS summary statistics, rare variant interpretation (pLoF/GoF), and functional genomics datasets such as eQTL and pQTL.

* Strong understanding of population genetics, genetic architecture, and variant-to-gene mapping.

* Ability to translate genetic association evidence into actionable therapeutic hypotheses.

* Excellent verbal and written communication skills and ability to collaborate in a cross-functional research environment.

Preferred

* Proficiency with statistical genetics and functional genomics tools such as REGENIE, SAIGE, TWAS frameworks, Mendelian randomization packages, and colocalization methods.

* Demonstrated ability to interpret pLoF/GoF variants and integrate eQTL/pQTL data with GWAS findings to identify causal genes and pathways.

* Experience developing AI- or LLM-assisted analytics for evidence synthesis, automation, or variant-to-gene mapping.

* Strong interest in translational genetics, RNAi therapeutics, and data-driven target discovery.