Computational Scientist II - Single Cell Genomics

Dawar Consulting•South San Francisco, CA

6d•Onsite

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.