Scientist/Senior Scientist (Genomic Analysis)

Preventive; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Preventive is hiring a Scientist or Senior Scientist to lead genomic analysis across wet-lab experimentation and computational pipelines, focusing on ultra-low-input NGS and multi-species samples with epigenetic characterization.

You will design, execute, and analyze experiments, build reproducible workflows, and work closely with genome-editing teams to ensure rigorous QC, safety profiling, and accurate interpretation of results.

Qualifications

• BS+ and 4+ years in a relevant field (demonstrable experience prioritized)
• Fluency in R or Python; experience analyzing NGS data and building reproducible workflows
• Proficiency in molecular biology techniques and sterile mammalian cell culture

Responsibilities

• Lead genomic analysis across wet-lab experiments and computational pipelines.
• Design, execute and analyze ultra-low-input NGS experiments across heterogeneous samples.
• Build and maintain reproducible analysis pipelines; perform QC, alignments, and variant calling.
• Biological interpretation of high-dimension NGS data and perturbation analysis.
• Collaborate with genome-editing teams on controls, study design, and QC.

Skills

R

Python

NGS data analysis

reproducible workflows

Molecular biology

Mammalian cell culture

Education

BS+ in a relevant field

Job description

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 genomic analysis across wet‑lab experimentation and computational pipelines. You will design, execute, and analyze ultra-low-input NGS experiments from heterogeneous, multi-species samples with emphasis on epigenetic characterization and comprehensive safety/off‑target profiling. The role spans low-input method development, specialized library prep, and computational analysis.

Key Responsibilities

• Characterization of edited samples: Execute plate-based single-cell/low-input NGS (e.g., Smart‑seq3/Smart‑seq2; plate-based scATAC/CUT&Tag; EMseq2) for genomic, epigenomic, and transcriptomic profiling of very small, heterogeneous samples where droplet methods are infeasible.
• Computational analysis: Build and maintain reproducible analysis pipelines; perform QC, UMI handling, multi-genome alignment, ambient RNA/doublet removal, batch correction/integration, differential analysis, trajectory/RNA velocity; support cross-species analyses (liftover/custom references).
• Biological interpretation: Design, defend and execute analyses of high-dimension NGS datasets to identify and validate perturbations from baseline biology; design experiments and benchmarks to compare strengths and limitations of NGS-based assays.
• Safety / off-target profiling: Genome-wide assessment of edited samples via WGS (short/long-read); call SNVs/indels/SVs/CNVs and quantify mosaicism/allele-specific edits.
• Experimental design & wet lab: Partner with genome-editing teams on controls and study design; design guides/donors; perform cloning and trace-input library prep with rigorous QC and documentation.

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.
• Proficiency in molecular biology (library prep, cloning, PCR/qPCR, nucleic-acid QC) and sterile mammalian cell culture.

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.
• Previous experience in a startup environment (comfort with fast cycles, evolving priorities, and cross-functional collaboration).