Scientist, Bioinformatics

Arc Institute•Palo Alto, CA

2d•Onsite

About The Position

The Arc Institute is seeking a Bioinformatics Scientist to join the Bioinformatics team within the Computational Technology Center. The successful candidate will play a crucial role in advancing the state-of-the-art in bioinformatics by developing, implementing, and applying computational approaches to high-throughput biological datasets. In this role, the Bioinformatics Scientist will contribute to major initiatives across the institute, working closely with experimentalists, technologists, and computational scientists to analyze complex sequencing and multi-omics data, support production-scale research efforts, and help translate data into actionable biological insights. This position offers the opportunity to support researchers across diverse scientific programs while helping build reliable, scalable, and reproducible bioinformatics workflows that accelerate discovery and enable Arc’s mission to better understand and treat complex human diseases.

Requirements

Ph.D. in Bioinformatics, Computational Biology, Computer Science, or related quantitative field.
1–2 years of post-Ph.D. experience in bioinformatics, computational biology, genomics, or related analysis of high-throughput sequencing datasets in an academic, biotech, pharmaceutical, or research institute setting.
Hands-on experience analyzing single-cell omics data, including scRNA-seq, scATAC-seq, and Perturb-seq, encompassing end-to-end workflows from raw data processing (alignment, quantification and QC) through dimensionality reduction, clustering, cell type annotation, trajectory inference, and differential expression analysis.
Hands-on experience leveraging AI-assisted development tools (e.g. Claude Code, GitHub Copilot, OpenAI Codex) to accelerate software development workflows, including automated code generation, refactoring, and review; and applying AI-assisted data analysis techniques to extract insights from large or complex datasets.
Demonstrated ability to collaborate effectively with wet-lab scientists, translating biological questions into computational analyses, communicating quantitative results to non-computational audiences, and iterating on analytical approaches in response to experimental findings; experience participating in cross-functional research teams spanning experimental design, data generation, and computational interpretation.
High competency with Python, git/GitHub, and Linux.
Strong statistical, mathematical, and data science skills.

Nice To Haves

Familiarity with CRISPR screen analysis, including Perturb-seq experimental design considerations, guide assignment, and perturbation effect modeling.
Scientific background in one or more disease-relevant areas, including neurodegeneration, cancer biology, or immunology.
Experience with cloud computing platforms (particularly GCP) for running production-scale pipelines.
Experience contributing to or maintaining open-source bioinformatics software, including writing tests, documentation, and versioned releases.
Comfort working with large imaging-based datasets or familiarity with image analysis pipelines is a plus.

Responsibilities

Implement, test, and maintain state-of-the-art analysis pipelines for various high-throughput projects using best software engineering practices.
Run the production pipelines for large-scale production datasets and provide quality assurance to experimentalists and technologists.
Analyze large-scale sequencing datasets (single-cell RNA-seq, epigenomics, multi-omics, Perturb-seq, spatial transcriptomics), public and generated in-house.
Process sequencing data as they come off instruments, monitoring runs, producing actionable results, and troubleshooting issues to ensure timely delivery.
Communicate analysis results to experimental and computational scientists.
Partner with experimentalists to troubleshoot analyses and help interpret pipeline outputs.
Manage code repositories on GitHub, document workflows, and maintain computational environments to ensure pipeline reliability.
Support multiple concurrent projects across different Technology Centers, requiring effective context-switching.