Scientist II, Single Cell Computational Biology

Company Mission
At eGenesis, we aspire to deliver safe and effective human transplantable cells, tissue and organs utilizing the latest advancements in genome editing.

Position Summary
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.

Primary Responsibilities

• Lead the design, analysis, and interpretation of single cell RNA-seq, ATAC-seq, and spatial transcriptomics experiments
• Integrate multi-modal datasets (e.g., scRNA-seq, CITE-seq, spatial transcriptomics) to uncover insights into tissue remodeling and immune responses
• Collaborate with cross-functional teams including wet lab scientists, immunologists, bioinformaticians, and translational scientists
• Develop and implement scalable pipelines and custom analytical tools for high-dimensional single cell datasets
• Interpret data in the context of immunological mechanisms and xenotransplantation
• Present findings to internal stakeholders and contribute to publications and patents
  
  
  
  
  

Basic Qualifications

• PhD with 3+ years of experience in Computational Biology, Genomics, Bioinformatics, Immunology, or a related field
• 2+ years of postdoctoral or industry experience analyzing single cell genomics data (scRNA-seq, scATAC-seq, spatial omics)
• 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
• Familiarity with popular single cell analysis tools (e.g., Seurat, Scanpy, Cell Ranger, Loupe, Squidpy, etc.)
• Demonstrated ability to work independently on complex data analysis problems and communicate results clearly to interdisciplinary teams