Computational Biologist, Contractor​/Consultant

About Preventive Medicine

Preventive is a public benefit corporation developing next-generation reproductive-genetics platforms to eliminate severe genetic disease at its origin. Our mission is to determine whether the newest generation of gene editing technologies can be used safely and responsibly to correct devastating genetic conditions for future children. If proven to be safe, we believe preventive gene editing could be one of the most important health technologies of the century.

About the role

Preventive is seeking a computational biologist contractor to develop rigorous, reproducible analysis pipelines supporting the safety and assessment of embryonic gene editing. The work will focus on low-input and single-cell studies involving gene-edited heterogeneous samples from multiple models, with an emphasis on distinguishing biologically meaningful findings from technical artifacts and establishing the reliability and limitations of analytical results.

You will collaborate closely with our wet-lab scientists to establish appropriate quality-control and statistical approaches and recommend improvements to experimental design and assay development based on analytical findings. As a contractor or consultant, you will provide ongoing computational biology support across active research programs, with analytical priorities evolving alongside the experimental work.

Key Responsibilities

• Computational analysis and strategy - End-to-end analysis of genomic, epigenomic, and transcriptomic data from raw reads and QC through statistical analysis, visualization, biological interpretation, and decision-ready reporting.
• Low-input and single-cell analysis - Analyze plate-based single-cell and trace-input datasets (ie scRNA- seq, low-input transcriptomic/epigenomic profiling). Establish appropriate quality-control criteria for these datasets.
• Safety and off-target profiling - Analyze WGS data from gene-edited samples to characterize intended and unintended editing outcomes, including SNVs/ indels, structural variation, copy-number changes, low-frequency mosaic variants, and potential off-target effects.
• Biological interpretation and experimental design - Work closely with scientists to define hypotheses, controls, replication strategies, acceptance criteria, and follow-up experiments. Distinguish technical artifacts from biologically meaningful effects and identify departures from baseline biology.
• Pipeline engineering and reproducibility: Build, test, document, and maintain reproducible workflows using version control, workflow orchestration, environment or container management, and appropriate compute infrastructure. Establish traceable data, metadata, software, and reporting practices suitable for rigorous preclinical research.
• Method evaluation and benchmarking - Develop quantitative benchmarks to compare NGS-based assays, analytical methods, and reference materials. Evaluate sensitivity, specificity, reproducibility, sources of uncertainty, and analytical limitations, and make recommendations for assay selection or validation.
• Regulatory rigor - Develop and validate analyses to a standard appropriate for preclinical safety assessment and potential regulatory review, with clear data provenance, predefined quality criteria, transparent reporting of limitations, and defensible analytical decision-making.
• 4+ years of relevant computational biology, bioinformatics, or genomics experience. We care more about demonstrable experience than formal education.
• Experience building and maintaining robust, reproducible bioinformatics pipelines on GCP, AWS, or comparable cloud infrastructure , including workflow orchestration, containerization, version control, and scalable compute.
• Fluency in R and/or Python
• Demonstrated expertise analyzing low-input and/or single-cell datasets (ie scRNA- seq, epigenomic profiling, long-read), including appropriate quality-control, normalization, statistical analysis, and handling of assay-specific technical artifacts.
• Strong statistical grounding and the ability to establish appropriate analytical frameworks for experimental comparisons, quantify uncertainty, evaluate data quality, and distinguish biological signal from technical variation.
• Experience with genomic variant analysis, including SNVs/indels, variant callers, and structural variation/CNVs, particularly in low-input, heterogeneous, or mosaic samples.
• Strong working knowledge of molecular biology and NGS assay…