Application Deadline: 19 October 2026

Department: Informatics

Location: London

Compensation: £55,000 - £70,000 / year

Description

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.

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

Responsibilities

• 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.