At a Glance
- Tasks: Analyse multi-omic datasets and run your own computational studies.
- Company: Join a pioneering biotech firm at the forefront of microbiome research.
- Benefits: Equity options, full health coverage, generous PTO, and a 401(k) match.
- Why this job: Make a real impact by bridging wet lab and AI in groundbreaking research.
- Qualifications: PhD or MSc with relevant experience in computational biology and programming skills.
- Other info: Be part of a small team with big ambitions and rapid career growth.
The predicted salary is between 60000 - 80000 ÂŁ per year.
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. Weâre working to narrow this gap. Weâre hiring a Computational Biologist to sit at the intersection of the wet lab and computational teams. Youâll 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. Weâre scaling our Boston screening platform and releasing a public foundation model this year. Youâre the person who makes our experimental output computationally actionable.
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. Youâre 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 lifeâscience field; or MSc with 3+ years of relevant industry experience.
- Handsâon experience analysing microbiome or multiâomic 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, versionâcontrolled, reproducible code; comfort with Git, Linux/commandâline environments, and cloud computing basics.
- Statistical rigor: multipleâhypothesis correction, compositional data analysis, batch effects. You know when a result is real vs. noise.
- Experience working effectively in a startup or other fastâmoving, resourceâconstrained 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 LCâMSâbased workflows. (This is a significant focus area for us.)
- Experience working closely with wet lab teams to coâdesign experiments with computational endpoints.
- Contributions to openâsource bioinformatics tools or community resources.
Why Join Outpost Bio?
- Youâll own real equity in what you build. We offer meaningful ISO 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 longâterm disability.
- An ML Labâinâtheâloop. Youâll 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 youâll iterate together.
- Small team, outsized reach. Youâre joining a small founding team backed by topâtier investors with deep connections across AI and Bio. The science you do here will directly shape how pharma and consumer companies understand moleculeâmicrobiome interactions.
Computational Biologist in London employer: Outpost Bio
Contact Detail:
Outpost Bio Recruiting Team
StudySmarter Expert Advice đ¤Ť
We think this is how you could land Computational Biologist in London
â¨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with researchers on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
â¨Tip Number 2
Show off your skills! Create a portfolio showcasing your analyses, projects, and any publications. This is your chance to demonstrate how you can bridge the gap between wet lab and computational work, making your application stand out.
â¨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with multi-omic data and how you've tackled challenges in previous roles. We want to see your thought process!
â¨Tip Number 4
Apply through our website! Itâs the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at Outpost Bio and contributing to our mission.
We think you need these skills to ace Computational Biologist in London
Some tips for your application đŤĄ
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Computational Biologist role. Highlight your hands-on experience with microbiome data and any relevant programming skills in Python or R. We want to see how you can bridge the gap between wet lab and computational work!
Craft a Compelling Cover Letter: Your cover letter is your chance to tell us why you're the perfect fit for this role. Share your passion for microbiome research and how your background makes you a great candidate. Donât forget to mention any independent scientific work you've done â we love seeing initiative!
Showcase Your Analytical Skills: In your application, be sure to highlight your experience with multi-omic data analysis and any statistical methods you've used. Weâre looking for someone who can demonstrate statistical rigor and a solid understanding of data quality issues, so make it clear how you've tackled these challenges in the past.
Apply Through Our Website: We encourage you to apply directly through our website. Itâs the best way for us to receive your application and ensures you donât miss out on any important updates. Plus, it shows us youâre keen on joining our team at Outpost Bio!
How to prepare for a job interview at Outpost Bio
â¨Know Your Multi-Omics Inside Out
Make sure you brush up on your knowledge of multi-omic datasets, especially metagenomics and metabolomics. Be ready to discuss specific examples from your past work where youâve analysed these types of data, and how you approached quality control and feature extraction.
â¨Show Off Your Coding Skills
Since strong programming skills in Python or R are crucial for this role, prepare to demonstrate your coding abilities. Bring along examples of clean, version-controlled code you've written, and be ready to explain your thought process behind building reproducible pipelines.
â¨Bridge the Gap Between Labs
Highlight your experience collaborating with wet lab teams. Think of specific instances where you designed experiments with computational endpoints in mind, and be prepared to discuss how you ensured smooth data handoff between teams.
â¨Prepare for Statistical Rigor
Brush up on your statistical analysis skills, particularly around multiple-hypothesis correction and compositional data analysis. Be ready to discuss how youâve tackled issues like experimental drift or batch effects in your previous projects, as this will show your understanding of data integrity.