Computational Biologist (London)

Mitch Mula - State Farm Agent; Greater London; Hybrid; GBP 50,000 - 70,000; Full time

Benefits offered by this job

Equity options

Medical/dental/vision coverage

Job summary

Outpost Bio is seeking a Computational Biologist to bridge wet-lab and computational teams in London. You will analyze proprietary multi-omics data, run computational studies, and build analytical foundations for our AI platform. The role emphasizes causal interpretation, reproducible pipelines, and collaboration across projects in a fast-moving startup.

Relocation is supported. You should have a PhD (or MSc with 3+ years) in computational biology or related field, with hands-on microbiome data

Qualifications

• Strong background in multi-omic data analysis and interpretation.
• Experience with microbiome or metagenomics data and pipelines.
• Proven ability to write reproducible, version-controlled code.

Responsibilities

• Analyze proprietary multi-omic datasets and run computational studies.
• Develop reproducible analytical pipelines and documentation.
• Collaborate with wet-lab, data engineering and ML teams.
• Ensure data quality and manage perturbation data between bench and models.
• Publish findings and contribute to model development feedback loop.

Skills

Python

R programming

Git

Linux/CLI

Cloud computing basics

Biology data analysis

Education

PhD in computational biology / bioinformatics / microbial genomics or related

MSc with 3+ years relevant industry experience

Tools

Nextflow

Snakemake

LC-MS workflows

Job description

Microbiome research today lives in two worlds. Assay-heavy labs build truth datasets but lack AI to model the complexity. AI teams build promising models on existing data, but can't understand causal mechanisms. We're working to narrow this gap. We're hiring a Computational Biologist to sit at the intersection of the wet lab and computational teams. You'll analyse proprietary multi-omic datasets generated in-house, run your own computational studies, and build the analytical foundations that feed directly into our AI platform. We're scaling our Boston screening platform and releasing a public foundation model this year. You're the person who makes our experimental output computationally actionable.

Relocation?

Yes

Compensation

£50k-£70k, depending on experience, plus equity & benefits

Location

London

Reports To

Saif Ur-Rehman (Director, Data Engineering)

Responsibilities

Multi-omic data analysis and interpretation. You own the computational analysis of datasets generated by our wet lab (metagenomics, metabolomics, 16S), from QC and feature extraction through statistical analysis and biological interpretation.

Computational studies. You run your own proof-of-concept investigations: microbiome-compound interactions, community dynamics, predictive biomarkers. You're a scientist with hypotheses, not a technician with a task list.

Perturbation data quality. You own the validation layer between the bench and the models. Experimental drift, contamination, batch effects, protocol deviations: you catch them before they corrupt model training.

Reproducible analytical infrastructure. As methods mature, you convert ad hoc analyses into robust, documented, version-controlled pipelines that others can run and extend. Your workflows become the standard.

Wet-dry lab bridge. You partner with the wet lab team to design experiments with computational endpoints in mind, and with the data engineering and ML teams in London to ensure smooth data handoff.

Your Background

PhD in computational biology, bioinformatics, microbial genomics, or a related quantitative life-science field; or MSc with 3+ years of relevant industry experience.

Hands-on experience analysing microbiome or multi-omic data (metagenomics, metabolomics, 16S) using common bioinformatics tools and pipelines, with strong programming skills in Python or R.

Track record of independent scientific work, demonstrated by publications, preprints, or equivalent outputs.

Ability to write clean, version-controlled, reproducible code; comfort with Git, Linux/command-line environments, and cloud computing basics.

Statistical rigor: multiple-hypothesis correction, compositional data analysis, batch effects. You know when a result is real vs. noise.

Experience working effectively in a startup or other fast-moving, resource-constrained environment.

Nice to Have

Experience building reproducible pipelines with Nextflow, Snakemake, or similar.

Familiarity with machine learning concepts and comfort collaborating with ML engineers on feature engineering or model evaluation.

Experience with metabolomics data processing or LC-MS-based workflows. (This is a significant focus area for us.)

Experience working closely with wet lab teams to co-design experiments with computational endpoints.

Contributions to open-source bioinformatics tools or community resources.

Why Join Outpost Bio?

You'll own real equity in what you build. We offer meaningful EMI 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. Full medical, dental, and vision coverage from day one (Outpost covers 100% of employee premiums). 401(k) with match. 25 days PTO plus your birthday off. Short- and long-term disability.

An ML Lab-in-the-loop. You'll run your own studies, publish findings, and see your analytical work feed directly into frontier ML models. The feedback loop between your analysis and the wet lab and AI platform is measured in days, not years: your data processing choices ripple through model performance, and you'll iterate together.

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-microbiome interactions.

About Us

Outpost Bio pairs high-throughput functional assays of human-derived microbial communities with causal machine learning to build predictive models of microbiome-compound interactions. Unlike black-box approaches, our Lab-in-the-Loop process tightly couples experimental design and AI development, so the models inform the science and the science inform the models. We generate large-scale perturbation datasets using stool-derived communities (SDCs) and multi-omic measurements, then train frontier models that predict metabolism, toxicity, and response at the community level. Headquartered in Boston and London, backed by Seedcamp, Merantix, Defined, and Openseed VC.