Sr. Biological Data Scientist

Role Summary

Senior Biological Data Scientist to join BridgeBio's Computational Genetics team. You will extract insights from large-scale population genomics data to inform new opportunities and internal development projects. The role combines computational genetics, statistical inference, machine learning, and simulation frameworks, and involves building and maintaining tools and infrastructure for streamlined, reproducible analyses. This is a US-based remote role with quarterly travel to the San Francisco office.

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 decision-making
• Derive insights through analysis of diverse data sources, including claims data, clinical trial information, business metrics, and drug development pipeline data
• 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

Qualifications

• PhD in Statistical Genetics, Human Genetics, Computational Biology, or related disciplines with 3+ years of industry experience
• 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
• Proficiency in the Python programing language
• Extensive experience with QC, analysis, and interpretation of human genetics data (single variant and gene-based association analysis using WES/WGS/array genotyping 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 working in Unix/Linux and cloud-based environments (e.g., AWS), with the ability to query and manipulate data from relational databases (e.g., SQL, Postgres)
• Working knowledge of version control using Git, including collaborative workflows such as branching, pull/merge requests, and code review
• Outstanding written and verbal communication skills in conveying analysis results to both experts and non-experts in the field
• Proven record of success by publications in peer-review journals and/or presentations at conferences
• You have demonstrated curiosity and adaptability in adopting AI-powered tools and technologies