Computational Biologist, Spatial Transcriptomics
Hybrid (San Francisco, CA)$140k - $170k + equityFull-timePosted 3w ago
About the role
Origin Bio is seeking a Computational Biologist to own post-run QC and secondary analysis of Xenium spatial transcriptomics data, including transcript-level and cell-feature outputs, aligned with post-run histology images (H&E).
Responsibilities
- 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.
- Collaborate with pathologists to integrate tumour regions and histological annotations into the spatial data.
- Analyse data beyond cell typing: investigate cell states, spatial neighbourhoods, tumour–immune interactions, differential expression and other emergent patterns from tissue data.
- Identify meaningful questions requiring data analysis; derive reference results and translate into reproducible tasks to evaluate AI model performance against underlying data.
Qualifications
- PhD or postdoctoral research in spatial biology or equivalent hands-on industry experience (Xenium or CosMx experience valued).
- Experience with oncology tissue analysis, H&E whole-slide images, or multiple imaging modalities (IHC, immunofluorescence).
- Strong Python or R skills and experience building reproducible analysis workflows.
- Experience with scRNA-seq or bulk RNA-seq analysis, including use of reference data to interpret spatial measurements.
- Sound statistical judgement: recognizing noise, batch effects, artefacts, confounding variables; ability to validate findings.
Skills
Required:PythonRData AnalyticsBioinformaticsBiology
Nice to have:Genomics
Last updated Oct 22, 2026. Interested? Apply on the company site.
About Origin
AI and Data for Cancer Therapeutics
Creating the largest patient-derived cancer single-cell dataset for licensing to pharmaceutical and AI companies. These datasets are unique as they screen the effects of various drugs and genetic medicines under the control of our DNA switches.
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