Scientist Computational Biology
Independent nonprofit AI-and-biology research institute developing biomedical AI models, data resources, and research tools.
About Arc Institute
Arc Institute is a Palo Alto-based nonprofit research organization founded in 2021. It supports long-term biomedical research and develops experimental and computational technologies, including virtual-cell models and open data resources, in partnership with Stanford, UC Berkeley, and UCSF.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will analyze Perturb-seq, single-cell, multi-omic, and functional genomics data to identify biological mechanisms. You will partner on study design and interpretation, develop reusable analysis tools and data resources, visualize findings, support predictive models, present results, contribute to scientific outputs, and mentor colleagues and interns.
Requirements
- PhD in computational biology, bioinformatics, genomics, systems biology, machine learning, computer science, molecular biology, or a related field
- 0–4 years of postdoctoral or professional research experience
- Experience deriving biological insight from large-scale single-cell, perturbational, or multi-omic datasets
- Experience with single-cell RNA-seq, Perturb-seq, CRISPR screens, single-cell ATAC-seq, or multiome data
- Experience with tertiary analysis, gene regulatory network inference, perturbation-effect modeling, or interaction modeling
- Expertise in Perturb-seq or CRISPR screen analysis at scale, or single-cell microglia analysis in neurodegeneration and Alzheimer's disease
- Python proficiency
- Experience with reproducible computational workflows, version control, and high-performance or cloud computing
- Ability to collaborate with experimental scientists
- Excellent written and verbal communication skills
- Minimum three days onsite per week
Responsibilities
- Analyze Perturb-seq, single-cell sequencing, multi-omic, and functional genomics datasets
- Model functional relationships among genes, regulatory programs, and cellular phenotypes
- Conduct guide assignment, perturbation-effect estimation, interaction modeling, trajectory analysis, or cell-state analysis depending on the selected track
- Partner with experimental teams on study design, analysis, interpretation, and validation
- Define handoffs from primary and secondary analysis into mechanistic modeling
- Generate, analyze, visualize, and interpret datasets for predictive models
- Develop reusable analysis notebooks, dashboards, software tools, benchmarks, and data resources
- Present findings and contribute to preprints or open-source projects
- Mentor colleagues and interns
- Work onsite in the Palo Alto office at least three days per week
Benefits
- Annual discretionary bonus