Senior Statistical Geneticist

What You'll Do
You will lead statistical genetics analyses of primarily population-scale genomics data to inform decisions about new business opportunities and advance internal development programs. Working independently in a dynamic, data-driven environment, you will leverage methods from multiple disciplines, including statistical inference, machine learning, and simulation frameworks, to extract insights and translate them into actionable recommendations. You will also contribute to building and maintaining the computational tools and infrastructure that enable streamlined, reproducible analyses across the team.

Responsibilities
- Design, execute, and interpret analyses of diverse quantitative and binary traits in large-scale population cohorts to support internal research programs and external opportunity evaluation (e.g., new program opportunities and partnership diligence)

- Perform integrative analyses of human genetics and EHR data to enable target identification, biomarker discovery, and clinical development decisions

- Collaborate closely with cross-functional stakeholders, including biology, clinical, and business development teams, to translate analytical findings into actionable insights

- Communicate results clearly and effectively through internal presentations, written reports, and contributions to external scientific publications and conferences

Where You'll Work
This is a U.S.-based remote role with periodic travel (quarterly or as needed) to BridgeBio’s San Francisco office or conference locations. 

Who You Are

Required:
- PhD in Statistical Genetics, Human Genetics, Computational Biology, or related disciplines with 5+ years of industry experience. Experience with rare disease genetics and translational research is strongly preferred.

- Demonstrated hands-on experience working with UK Biobank and All of Us data, including a strong understanding of their data architecture and data types. Applicants must include brief descriptions of relevant projects in their resume

- Extensive experience with QC, analysis, and interpretation of human genetics data (single-variant and gene-based association analyses using WES/WGS/array genotyping data) 

- Proficiency in Python and hands-on experience integrating AI-powered tools (e.g., Claude, Codex) into analytical workflows to improve productivity, reproducibility, and code quality across code generation, data analysis, and documentation, with appropriate validation of outputs.

Preferred

- Experience inferring disease incidence and prevalence from genetics data

- Familiarity with large-scale human genomics databases (including but not limited to gnomAD, GTEx, 1000 Genomes, and TOPMed). Hands-on experience is strongly preferred

- Experience supporting target validation and/or biomarker discovery efforts in a pharmaceutical or biotechnology setting.

- Familiarity with multi-omics data types (e.g., transcriptomics, proteomics, epigenomics, chromatin accessibility); hands-on experience preferred.

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