Scientist, Computational Biology

Arc Institute•Palo Alto, CA

1d•$135,000 - $186,500•Hybrid

About The Position

We are hiring for two Scientist positions on Arc's Computational Technology Center, each turning large-scale perturbational and single-cell datasets into mechanistic biological insight. The roles are situated at the interface of functional genomics, computational biology, and machine learning. Successful candidates will analyze and model data from Perturb-seq, single-cell and multi-omic sequencing, lineage tracing, chemogenetic screens, and related high-throughput experimental approaches. Both roles are highly collaborative, partnering closely with experimental scientists, bioinformatics infrastructure teams, machine learning researchers, and Arc investigators to identify biological mechanisms, nominate targets, and guide the design of future experiments. There are two focus areas: Perturb-seq / functional genomics track — focused on large-scale CRISPR screen and Perturb-seq analysis, contributing to the Virtual Cell Initiative (VCI). Neurobiology / microglia track — focused on single-cell analysis of microglia and neurodegeneration, contributing to the Alzheimer's Disease Initiative (ADI).

Requirements

PhD in computational biology, bioinformatics, genomics, systems biology, machine learning, computer science, molecular biology, or a related field, with 0–4 years of postdoctoral or professional research experience.
Demonstrated track record of deriving biological insight from large-scale single-cell, perturbational, or multi-omic datasets.
Hands-on experience with single-cell, perturbational, or multi-omic data, e.g. single-cell RNA-seq, Perturb-seq or CRISPR screens, single-cell ATAC-seq or multiome, including tertiary analysis, such as gene regulatory network inference, perturbation-effect or interaction modeling.
Expertise in at least one of the following: (a) Perturb-seq / CRISPR screen analysis at scale, including guide assignment, perturbation-effect estimation, interaction modeling, batch correction, and interpretation of pooled genetic screens; or (b) Single-cell analysis of microglia, in the context of neurodegeneration and Alzheimer's disease biology.
Python proficiency, with experience using modern scientific computing and data analysis ecosystems.
Familiarity with reproducible computational workflows, version control, and high-performance or cloud computing environments.
Biological intuition and the ability to collaborate effectively with experimental scientists.
Excellent written and verbal communication skills, with a track record of publications, preprints, open-source tools, or other scientific outputs.
Ability to work in a fast-paced, ambitious, interdisciplinary research environment.
Work a minimum of 3 days onsite in our Palo Alto office.

Nice To Haves

Background in cell identity, reprogramming, RNA biology, cell engineering, neurobiology, immunology, cancer biology, or complex disease genetics.
Familiarity with dimensionality reduction and gene module analysis techniques in the context of single-cell biology.
Experience in a startup, technology center, research institute, or other highly collaborative environment where scientific direction and technical execution are tightly coupled.

Responsibilities

Conduct tertiary analyses of large-scale Perturb-seq, single-cell sequencing, multi-omic, and functional genomics datasets to identify functional relationships between genes, regulatory programs, and cellular phenotypes.
Partner with experimental teams on iterative study design, analysis, interpretation, and validation, discovering computational insights that translate into testable biological hypotheses.
Work closely with bioinformatics and data infrastructure teams to define clean handoffs from primary and secondary analysis into exploratory and mechanistic modeling.
Contribute to Arc's Virtual Cell and Alzheimer's Disease Initiatives by generating, analyzing, visualizing, and interpreting datasets that fuel predictive models.
Develop reusable analysis notebooks, dashboards, software tools, benchmarks, and data resources that allow Arc scientists to explore complex datasets effectively.
Present findings to internal stakeholders, and contribute to preprints or open-source projects when the opportunity arises or as needed.
Mentor colleagues and interns and contribute to a collaborative, intellectually rigorous team environment.