Computational Biologist

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 hiring a Scientist or Senior Scientist to lead computational genomic analysis for low-input and single-cell studies of gene-edited, heterogeneous, and multi-species samples. This role will be responsible for analytical strategy, pipeline development, data quality, and biological interpretation across genomic, epigenomic, and transcriptomic assays.

You will partner closely with experimental scientists to shape study design, define controls, understand assay constraints, and turn complex sequencing data into clear decisions. This role will also have the opportunity for hands-on wet-lab work. Candidates with limited or past bench experience who are motivated to learn the relevant methods are encouraged to apply.

Key Responsibilities

• Computational analysis and strategy: Own end-to-end analysis of genomic, epigenomic, and transcriptomic data, from raw reads and quality control through statistical analysis, visualization, biological interpretation, and decision-ready reporting.
• Low-input and single-cell data: Analyze plate-based single-cell and trace-input assay for samples. Develop fit-for-purpose approaches for UMI handling, sparse data, low cell counts, contamination, ambient RNA, doublets, and other assay-specific considerations.
• Safety and off-target profiling: Genome-wide assessment of edited samples via WGS
• Biological interpretation and experimental design: Work with genome-editing and assay-development teams to define hypotheses, controls, replication, acceptance criteria, and follow-up experiments. Identify and validate departures from baseline biology, and distinguish technical artifacts from biologically meaningful effects.
• 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, analysis methods, and reference materials. Communicate performance limits, sources of uncertainty, and recommendations for assay selection or validation.
• Cross-functional communication: Present analytical plans and results to computational, experimental, and leadership audiences; contribute code reviews, data reviews, technical documentation, and clear written conclusions.
• Targeted wet-lab engagement: Learn the practical constraints of relevant low-input workflows through observation, structured cross-training, and hands-on support with activities such as library preparation, PCR or qPCR, and embryology. Benchwork is not the primary focus of this role, but will occur consistently.

Qualifications

Minimum qualifications

• BS+ and 4+ years in a relevant field (we care more about your demonstrable experience than your formal education).
• Fluency in R or Python; experience analyzing NGS data (alignment, QC, variant calling) and building reproducible workflows.
• Demonstrated expertise with low-input/single-cell assays (e.g., scRNA-seq, epigenomic profiling, long-read).
• Working knowledge of molecular biology and NGS assay principles.

Preferred qualifications

• One or more of the following:

• End-to-end off-target discovery/validation for gene-edited samples in preclinical studies, leading to submission to regulatory bodies
• Single-cell analysis beyond defaults (batch correction, trajectory/velocity, doublet/ambient handling in low-cell-number datasets).
• Genome-wide variant analysis for edited samples (SNVs/indels/SVs/CNVs; low-VAF mosaic detection; integration-site mapping) and epigenomic characterization.
• Experience with very early developmental or gamete samples across species.
• Spatial transcriptomics/epigenomics

• Prior exposure to library preparation, PCR or qPCR, nucleic-acid quality control, or mammalian cell culture.
• Previous experience in a startup environment (comfort with fast cycles, evolving priorities, and cross-functional collaboration).