Scientist II, Computational Biology (Single Cell & Spatial)

ABOUT eGENESIS

PRIMARY RESPONSIBILITIES

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.

BASIC QUALIFICATIONS

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.