Computational Biologist, Spatial Transcriptomics

About the roleOrigin produces matched spatial transcriptomics, histopathology images and clinical datasets from real patient tumours, using our own laboratories. We’re looking for a computational biologist to own the quality assessment and biological interpretation of our Xenium data. You’ll build reproducible workflows, connect molecular measurements with tissue morphology, and help shape how we generate and use these datasets for cancer research and AI evaluation.What you’ll work onDevelop reproducible QC workflows and define sample- and run-level quality criteria for transcript detection, background signal, cell segmentation, transcript assignment, and tissue or imaging artefacts.Align post-run H&E whole-slide images to Xenium DAPI images and assess registration accuracy across each tissue section.Work with pathologists to bring tumour regions and other histological annotations into the spatial data.Work with laboratory scientists to interpret unexpected results, distinguish technical artefacts from biological variation, and use findings to improve future experiments and data generation.Analyse the data beyond cell typing: investigate cell states, spatial neighbourhoods, tumour–immune interactions, differential expression and other patterns that emerge from the tissue. Use relevant single-cell reference datasets where helpful.Formulate biological questions that can be answered from our datasets. For selected questions, develop reproducible reference analyses, document the supporting evidence and uncertainty, and define criteria for evaluating AI-generated analyses and conclusions.Helpful past experiencePhD or postdoctoral research in spatial biology or equivalent hands-on industry experience. Xenium or CosMx experience is especially valuable; experience with other spatial platforms is welcome.Analysis of oncology tissue, H&E whole-slide images, or multiple imaging modalities such as IHC and immunofluorescence.Strong Python or R skills and experience building reusable analysis workflows across multiple samples, with version control, reproducible environments and clear documentation.Experience with scRNA-seq or bulk RNA-seq analysis, including the use of reference data to interpret spatial measurements.Sound statistical judgement: recognising noise, batch effects, experimental artefacts and confounding variables, and knowing when an apparent biological finding needs further validation.This is a full-time role. We prefer someone who can work with us in person in San Francisco, but we’re open to the right person working remotely.