Scientist II, Computational Biology (Single Cell & Spatial)

We are seeking a highly skilled and motivated Scientist II with expertise in single cell and spatial genomics data analysis. The ideal candidate will play a key role in unraveling the cellular and spatial architecture of engineered organs and immune interactions in our translational research programs. This is a unique opportunity to drive high-impact research at the intersection of genomics, immunology, and synthetic biology.

• Identify and frame open biological questions across our programs, define the analytical strategy to address them, and set your own priorities with minimal day-to-day direction.
• Lead the design, analysis, and interpretation of single cell RNA-seq and spatial transcriptomics experiments.
• Integrate multimodal datasets, including spatial transcriptomics, scRNA-seq, proteomics, metabolomics, pathology and clinical metadata, to uncover insights into tissue remodeling and immune responses.
• Collaborate with cross-functional teams including wet lab scientists, immunologists, bioinformaticians, clinicians and translational scientists.
• Develop scalable pipelines for high-dimensional single cell and spatial datasets, and build new analytical approaches where existing tools fall short (e.g., cross-species cell mapping, sparse or incomplete reference annotations).
• Perform spatially resolved analyses of cell states, tissue architecture, cell-cell interactions, and molecular programs associated with graft injury, inflammation, remodeling, and repair.
• Translate biological and translational questions into computational analyses and testable hypotheses, with interpretation grounded in immunological mechanisms and xenotransplant biology.
• Present findings to internal stakeholders and contribute to publications and patents.

• PhD in Computational Biology, Genomics, Bioinformatics, Immunology, or a related field.
• 3+ years of postdoctoral or industry experience analyzing single cell and spatial data, including scRNA-seq and spatial transcriptomics.
• Demonstrated experience leading computational projects from experimental design and data QC through biological interpretation and communication of results.
• Strong proficiency with R and/or Python for statistical computing and data visualization.
• Deep understanding of immune cell biology and ability to interpret immune-related transcriptional signatures.
• Hands-on experience analyzing spatial transcriptomics data from at least one sequencing-based or imaging-based platform (e.g., Visium/Visium HD, Xenium, Trekker, Seeker); experience integrating across platforms is a strong plus. Candidate should have an understanding of platform-specific strengths, limitations, and analytical considerations.
• Fluency with standard single cell and spatial analysis tools (e.g., Seurat, Scanpy, Cell Ranger, SpatialData, Squidpy).
• Practical experience using AI tools (e.g., LLM-based coding assistants and agents) to speed up analysis and software development, with the judgment to check AI-generated code and results critically.
• Track record of independently defining and answering open research questions, where the question, approach, or method was not set in advance, as shown by first-author publications, novel methods, or equivalent industry work.