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Single Cell Genomics Data Science Jobs (NOW HIRING)

Merck & Co.

$64K - $65K/yr

A proven track record of applying machine learning to analyze single cell RNA sequencing data to ... Science, Mathematics, Biophysics, Genetics/Genomics, or a related STEM field with 4+ years of ...

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As of Sep 9, 2026, the average hourly pay for single cell genomics data science in the United States is $35.51, according to ZipRecruiter salary data. Most workers in this role earn between $29.09 and $40.38 per hour, depending on experience, location, and employer.

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Infographic showing various Single Cell Genomics Data Science job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 83% Full Time, 12% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $73,866 per year, or $35.5 per hour.

Computational Scientist II - Single Cell Genomics

South San Francisco, CA • On-site

Dawar Consulting
IT Services • 11 - 50 employees
Learn more about Dawar Consultingexternal

$80 - $100/hr

Other

Medical, Dental, Vision, Retirement

Re-posted 3 days ago

Job description

Computational Scientist II - Single Cell Genomics

South San Francisco, United States | Posted on 07/08/2026

Our Client, a world leader in Biotechnology is looking for a Computational Scientist II for SSF, CA

Job Duration: Long Term Contract (Possibility Of Extension)

Pay Rate: $65/hr on W2

Company Benefits: Medical, Dental, Vision, Paid Sick leave, 401K

This role is focused on advancing therapeutic discovery through high-content perturbation screening and single-cell genomics. This role will involve analyzing large-scale sequencing datasets, developing computational pipelines, and collaborating with multidisciplinary teams to generate biological insights that support drug discovery.

Key Responsibilities
  • Analyze and interpret large-scale single-cell sequencing datasets (scRNA-seq) generated from high-content perturbation experiments.
  • Develop and optimize computational workflows for Perturb-seq, CROP-seq, Sci-Plex, and other sequencing-based functional genomics studies.
  • Apply statistical and computational methods to identify biological mechanisms, therapeutic targets, and treatment responses.
  • Collaborate with biologists, chemists, computational scientists, and cross-functional research teams to translate complex data into actionable insights.
  • Develop reproducible data analysis pipelines using Python and bioinformatics tools.
  • Perform quality control, data integration, visualization, and statistical analysis of large-scale genomics datasets.
  • Integrate multimodal datasets, including single-cell, genomic, and clinical data, to support research and therapeutic development.
  • Present findings through scientific reports, presentations, and collaborations with internal research teams.
  • Maintain well-documented, reproducible computational workflows and contribute to continuous process improvements.
Required Qualifications
  • Ph.D. in Computational Biology, Bioinformatics, Computer Science, Statistics, Mathematics, or a related quantitative life science discipline.
  • Proven experience analyzing large-scale single-cell RNA sequencing (scRNA-seq) datasets.
  • Strong programming skills in Python for scientific computing and data analysis.
  • Solid background in statistics, probabilistic modeling, and computational data analysis.
  • Experience working with next-generation sequencing (NGS) and genomics datasets.
  • Excellent analytical, communication, and problem-solving skills.
  • Demonstrated ability to collaborate effectively in multidisciplinary research environments.
  • Strong publication record demonstrating scientific contributions.
Preferred Qualifications
  • Experience with Perturb-seq, CROP-seq, Sci-Plex, or other CRISPR-based perturbation screening technologies.
  • Experience with CRISPR functional genomics and single-cell perturbation analysis.
  • Knowledge of multimodal data integration, including genomic, transcriptomic, and clinical datasets.
  • Experience using workflow management systems such as Nextflow or Snakemake.
  • Experience working on High Performance Computing (HPC) environments using SLURM.
  • Familiarity with cloud computing, reproducible workflows, and collaborative software development practices.

If interested, please send us your updated resume at

Single Cell Sequencing, Pertub-Seq, Slurm, HPC, Computational Biology

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