Description review
Computational Biologist, Spatial Transcriptomics
Origin · San Francisco, California, US · back to the listing
HR standards
52/100
needs work
Title ↔ description
81/100
solid
Reads as
Data Scientist
96% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- No section describes what the person would actually do. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
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Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
Origin Bio is producing matched spatial transcriptomics, histopathology images and clinical datasets from real patient tumours. We’re looking for a computational biologist to own post-run QC and secondary analysis of our Xenium data, from transcript-level and cell-feature outputs through to datasets aligned with post-run H&E images.
What you’ll work on
• Develop reproducible QC workflows for transcript detection, background signal, cell segmentation, transcript assignment, tissue and imaging artefacts, and variation across samples and runs.
• 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.
• 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.
• Identify meaningful questions that require analysis of the data to answer. For selected questions, derive well-supported reference results and turn the work into reproducible tasks that evaluate whether AI models can reach those results from the underlying data.
Helpful past experience
• PhD 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 reproducible analysis workflows.
• 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.
What you’ll work on
• Develop reproducible QC workflows for transcript detection, background signal, cell segmentation, transcript assignment, tissue and imaging artefacts, and variation across samples and runs.
• 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.
• 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.
• Identify meaningful questions that require analysis of the data to answer. For selected questions, derive well-supported reference results and turn the work into reproducible tasks that evaluate whether AI models can reach those results from the underlying data.
Helpful past experience
• PhD 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 reproducible analysis workflows.
• 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.