Origin Verified
Chemicals, Materials Science, Cleantech
Computational Biologist, Spatial Transcriptomics
San Francisco, CAHybridFull Time$140,000–$170,000 /yrPosted 1 month agoVisa sponsorship availableHidden Gem · YC Startup
Required skills
- Xenium
- Python
- Reproducible Workflows
- Statistical Judgement
- Image Analysis
- Histopathology
- Cell Segmentation
- Transcript Detection
- Differential Expression
Nice to have
- Postdoctoral Research
- Spatial Biology
- CosMx
- Oncology Tissue
- H&E Whole-Slide Images
- IHC
Role summary
Origin Bio is seeking a Computational Biologist with expertise in spatial transcriptomics to manage post-run QC and secondary analysis of Xenium data. The role involves developing reproducible workflows, aligning imaging data, integrating histological annotations, and performing advanced analyses such as cell state investigation and tumour-immune interactions. Experience with Python or R, scRNA-seq/bulk RNA-seq, statistical judgment, and image analysis is required. Experience with Xenium/CosMx and oncology tissue analysis is highly valued. This full-time position can be hybrid in San Francisco or remote.
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.
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