Senior Bioinformatician
$214,400–$245,000 year
On-siteSan Francisco, California, United States
Job Summary
Design, implement, and critically evaluate computational approaches for single-cell, bulk transcriptomics, secretomics, and proteomics data, including QC, normalization, batch correction, and multi-dataset integration. Architect end-to-end analysis workflows in Python and R using modern orchestration tools to process tens to hundreds of thousands of samples, ensuring reproducible, version-controlled pipelines. Own dataset analysis to answer biological and client-driven questions, partnering with experimental biologists to frame relevant inquiries and produce publication-quality figures and decision-useful reports. Translate complex multi-omic analyses into clear insights for cross-functional teams while driving benchmarking strategies for internal tissue model development.
Required Qualifications
- Ph.D. in Computational Biology, Bioinformatics, Genomics, Systems Biology, Biostatistics, or a related quantitative field with deep focus on transcriptomics and/or proteomics
- 2–5 years of post-PhD experience
- Strong, hands-on proficiency in Python for bioinformatics (Scanpy and the broader scverse ecosystem in particular)
- Familiarity with R-based workflows (Seurat, Bioconductor, Monocle)
- Deep experience with scRNA-seq datasets and workflows, including QC, batch correction, clustering, differential expression, annotation, and integration across experiments
- Experience with proteomics or secretomics analysis (Olink or similar high-dimensional panels), including normalization and cross-modality integration with transcriptomic profiles
- Comfort in a Unix/Linux environment with standard dev tools (git, containers, CI/CD)
- Experience with cloud environments (AWS)
- Experience with workflow managers
- Familiarity with data infrastructure for large-scale biological data (e.g., AnnData/H5AD, TileDB-SOMA, or similar)
Desired Qualifications
- some industry experience
- experience with multi-modal data integration (e.g., combining imaging, transcriptomics, and proteomics features)
- Background in drug discovery, toxicology, or translational research where multi-omic data inform therapeutic decisions
- Experience with representation learning or ML for biological data (embeddings for cells, genes, or proteins), especially in collaboration with ML specialists
- Familiarity with organoids, primary tissues, or organ-on-chip systems
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