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eGenesis is a clinical-stage biotechnology company developing human-compatible engineered organs to address the global organ shortage. The company uses proprietary genome engineering to make extensive multiplex gene edits, add protective human transgenes, and inactivate endogenous retroviruses. Their lead program, EGEN-2784 (a genetically engineered porcine kidney), is in a multi-patient Expanded Access study at MGH.
As Scientist II, Computational Biology, you will identify and frame open biological questions across programs, define analytical strategies, and set priorities with minimal day-to-day direction. You will lead the design, analysis, and interpretation of single cell RNA-seq and spatial transcriptomics experiments. Key responsibilities include integrating multimodal datasets (spatial transcriptomics, scRNA-seq, proteomics, metabolomics, pathology, clinical metadata) to uncover insights into tissue remodeling and immune responses. You will develop scalable pipelines for high-dimensional datasets and build new analytical approaches where existing tools are insufficient (e.g., cross-species cell mapping, sparse reference annotations). You will perform spatially resolved analyses of cell states, tissue architecture, cell-cell interactions, and molecular programs associated with graft injury, inflammation, remodeling, and repair.
You will collaborate with cross-functional teams including wet lab scientists, immunologists, bioinformaticians, clinicians, and translational scientists. You will translate biological and translational questions into computational analyses and testable hypotheses, grounded in immunological mechanisms and xenotransplant biology. You will present findings to internal stakeholders and contribute to publications and patents.
REQUIREMENTS:
- PhD in Computational Biology, Genomics, Bioinformatics, Immunology, or related field
- 3+ years of postdoctoral or industry experience analyzing single cell and spatial data (scRNA-seq, 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 (Visium/Visium HD, Xenium, Trekker, Seeker); experience integrating across platforms is a strong plus
- Understanding of platform-specific strengths, limitations, and analytical considerations
- Fluency with standard single cell and spatial analysis tools (Seurat, Scanpy, Cell Ranger, SpatialData, Squidpy)
- Practical experience using AI tools (LLM-based coding assistants and agents) to speed up analysis and software development, with critical judgment to verify AI-generated code and results
- Track record of independently defining and answering open research questions (not pre-set), demonstrated by first-author publications, novel methods, or equivalent industry work
