Data Scientist (1-year fixed-term contract)
Portal Biotech
London, United KingdomPosted Jun 23, 2026
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Bioinformatics (BiX)
·
London
Data Scientist (1-year fixed-term contract)
Apply statistics and machine learning on raw signals acquired from Portal's protein sequencing platform to identify and characterise proteins, working collaboratively with the wet lab
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About the rolePortal is seeking a motivated Data Scientist to join our Bioinformatics & Machine Learning team. In this 1-year, fixed-term role, you will work directly with the wet lab, applying techniques from statistics and machine learning to invent new applications of Portal's single-molecule proteomics platform. The work spans the full range from exploratory data analyses to shipping production-hardened methods, and is embedded in a fast-moving, highly collaborative research environment at the scientific frontier of the field. This role is ideal for someone who is excited by novel methodological challenges, applies scientific rigour and first principles approach to their work, and is comfortable in rapidly evolving and often ambiguous problem spaces.Tasks and responsibilitiesResearch, development, and shipping of analytical pipelines for nanopore-based proteomic dataDesign and implementation of statistical and ML approaches to novel biological data, iterating from exploratory analysis through to deployable pipeline componentsRobust evaluation and benchmarking of algorithmic approaches, with clear documentation of findingsLiaise with experimental scientists and engineers to understand data characteristics, interpret results, and align on deliverablesPresent analytical results and methodological decisions to multidisciplinary audiences, including non-technical stakeholders and clientsContribute to a culture of scientific rigour, reproducibility, and code quality within the team and outwards to the entire companyExperience & requirementsQualificationsMaster's degree or higher in Bioinformatics, Statistics, Machine Learning, or a related fieldTechnicalStrong theoretical grounding in statistics and ML, with the ability to apply it...