Computational Biologist II
Own day-to-day analysis for bulk RNA-seq, single-cell, and perturbation datasets inside a discovery group that already generates more data than it consistently interprets.
About the role
Take ownership of the analysis rhythm for active discovery programmes and make results decision-ready for weekly science reviews. This role is not purely service work: you will help decide what gets rerun, what gets dropped, and where better computational structure would materially improve how the biology team works.
Role details
- Stage
- Series B therapeutics team, 41 people, 6 in computational biology and data
- Reporting line
- Head of Computational Biology
- Rhythm
- Three days on site in South San Francisco, two days flexible
What you'll do
- Run high-value analyses without hand-holding: Own analysis for transcriptomics and perturbation datasets from raw output through QC, interpretation, and readout packages that scientists can actually use in programme discussions.
- Tighten reproducibility where it matters: Turn fragile notebook-heavy work into repeatable workflows for the assays that recur often enough to justify stronger process.
- Work closely with wet-lab scientists: Help experimental teams frame questions that the data can answer cleanly, and say so directly when the design will not support the conclusion being asked of it.
- Improve the signal path to decisions: Make sure programme leads get outputs with enough context, caveats, and prioritisation to act quickly rather than wait for another round of interpretation.
Requirements
- PhD in computational biology, bioinformatics, genomics, systems biology, or a related field, or an MSc plus clearly comparable industry depth.
- Hands-on experience with RNA-seq and at least one of single-cell, CRISPR screen, or other high-dimensional biological datasets.
- Strong coding ability in Python and R, with evidence that you can move beyond exploratory analysis into maintainable scientific workflows.
- Comfort working directly with bench scientists in a fast-moving setting where priorities can change between one week and the next.
- Good scientific judgement under ambiguity, especially when the data is only good enough to support a practical next step rather than a perfect answer.
Nice to have
- Experience with Scanpy, Seurat, or workflow tooling such as Nextflow or Snakemake.
- Exposure to spatial data, perturbation data, or multimodal assay readouts.
- Previous time in a biotech startup where process had to be built as you went.
- Working familiarity with cloud storage conventions, SQL, or lightweight internal tooling.
Tools and environment
Python, R, Scanpy, Seurat, SQL, AWS, Nextflow, Docker
Compensation and package
- Base salary
- $125k-$145k, depending on dataset depth and startup fit
- Bonus
- 10 percent annual target bonus plus stock options
- Benefits
- Medical, dental, 401(k), commuter support, and annual learning budget
- Timing
- Team wants someone in seat before the next programme expansion cycle
Interview process
- 1.Recruiter screen: Initial call covering location, compensation, and whether your experience is more platform-facing or programme-facing.
- 2.Hiring manager deep dive: Technical conversation on your analysis approach, judgement around weak data, and how you work with experimental teams.
- 3.Case review: Discuss an anonymised dataset and explain how you would structure QC, interpretation, and the final recommendation.
- 4.On-site final: Cross-functional meetings with biology and platform stakeholders, followed by references and offer discussion.
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