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This listing scored 54/100, which is a D. It lost the most ground on pay transparency.
- Description depth 20 / 20 How much the posting actually says about the work, measured in characters of real text.
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- Remote clarity 8 / 15 Whether "remote" means anywhere, or is quietly restricted to one country.
- Role specificity 6 / 10 Whether the listing is tagged well enough to tell what the role actually is.
- Corroboration 5 / 10 Whether more than one source carries this listing.
- Pay transparency 0 / 25 A published salary range, worth more than any other single factor because it is what a candidate cannot find out without applying.
Every figure above is arithmetic over the posting itself — its salary field, its text, its age, its tags and how many sources carry it. How the grades work →
yc-startup vc-backed
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.
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Where this listing came from
- 29 Sep 2026 Y Combinator first sighting
Seen on 1 board over 4 days.