Computational Biologist

• A relevant doctorate, or a master's degree plus at least three years of industry work. Evidence of independent research through papers, preprints or comparable scientific outputs.
• Hands-on experience analyzing metagenomic or 16S data, with a good understanding of microbial biology and the limitations of reference databases and functional annotations.
• Sound statistical reasoning, including experimental design, confounding, repeated measurements, multiple testing and the challenges of compositional data.
• Proficiency in Python and/or R, with experience writing reproducible analysis code using Git and Linux. Other people should be able to understand and rerun your work.
• Comparative genomics, pangenomes, enzyme or pathway annotation, or sequence-based methods.
• Experience working with experimental scientists. You can explain what the data supports, challenge an unsupported interpretation and propose a practical way to resolve uncertainty.

• Nice to have: hands-on LC-MS data processing; experience with microbial metabolism or drug biotransformation; biological machine learning, evaluation of learned representations, or large-scale analyses using cloud compute; building shared workflows with Nextflow or Snakemake, or contributing to community bioinformatics software.

• You'll own real equity in what you build. We offer meaningful stock options because we believe the people building this company should share in what it becomes. We want teammates who think like owners, and we structure compensation to reflect that.
• Outstanding benefits. Private medical and dental with Bupa from day one, paid by Outpost, and dependents covered at 50%. Pension with a 3% employer contribution. Life insurance, income protection and critical illness cover, all employer paid. Cycle to Work scheme. 25 days holiday plus bank holidays, your birthday off, and a paid winter break between Christmas Eve and New Year.
• An ML Lab-in-the-Loop. Your work feeds directly into Outpost's AI platform, and the platform feeds back into the next experiment. The loop between the wet lab, the data and the models runs in days, not years, and you'll iterate inside it whichever side you sit on.
• How we work. Expected in the office Monday, Wednesday and Friday, with flexible hours around a 10am to 4pm core and up to two weeks a year working from anywhere. Home office stipend and company computer. We cover conference costs and want you presenting your work, and every quarter you get a budget for drinks or coffee to learn from peers at other companies.
• Small team, outsized reach. You're joining a small founding team backed by top-tier investors with deep connections across AI and bio. The science you do here will directly shape how pharma and consumer companies understand molecule and microbiome interactions.

Outpost Bio is building models of how microbial communities respond to drugs and other chemicals. We are looking for a computational biologist to turn microbiome data into biological understanding and methods our scientists can rely on. You will own computational studies and the scientific decisions that connect experimental measurements to model inputs.

• Run the standard analyses on each experimental screen, calibrate them against controls and reference data, and keep them consistent as throughput grows.
• Assess every dataset before it is used for model training and/or decision making for further experimentation. Investigate contamination, assay drift, protocol changes and batch structure, document what is and isn't fit to learn from, and feed that back to the wet lab.
• Bring new data types into the same workflows as the wet lab adds them, metagenomics and in-house mass spectrometry among them, under the guidance of the scientists who own those methods. Work with the Data Platform Engineer to make validated methods repeatable.
• Take the computational tools we have already built to the point where the team uses them routinely, and document the assumptions behind them.
• Develop and validate methods to analyze 16S and metagenomic data from our experiments, collaborators and public studies. Make assumptions, quality checks and limitations explicit.
• Investigate responses to drugs and other chemicals across donors, communities and experimental conditions, using statistical controls that separate biological effects from batch effects, sampling and other confounders.
• Work with experimental scientists to refine hypotheses and design controls, replication and follow-up experiments, and with our computational scientists and ML researchers to define biologically meaningful features and benchmarks and investigate where models fail.
• Contribute to existing research initiatives and propose your own in-silico questions as the data supports them. Write reusable analysis code and explain results clearly to colleagues with different scientific backgrounds.

We're hiring one person for this role, and they can be based in either London or Boston. Apply to the posting for the city you'd work from. You only need to apply once.

Here is our timeline for hiring this role:

• Now until October 19th: Accepting applications
• October 26th: Planned start for interviews