The Cell Therapy and Synthetic Immunity group addresses computational challenges associated with
immunotherapies that primarily utilizes cell and gene therapy. The group is seeking a motivated and
detail-oriented Computational Scientist to join the team to address both platform and target biology
related computational needs. This role will interface with a multidisciplinary team of scientists focused
on developing innovative therapies and enhancing our understanding of cellular therapies.
Location
- Remote location in US and Canada OK. Must be available during PST working hours.
Key Responsibilities
- Integrate multi-modal datasets (e.g., scRNA-seq, CITE-seq, spatial transcriptomics) to uncover
insights into tumor micro-environment and immune responses pre/post treatment
- Develop computational approaches to integrate large single cell datasets to generate
reference maps/cell atlases.
- Utilize gene signatures to interpret changes in composition of cell-types and/or their functional
states in longitudinal datasets
- Analyze large genomic datasets (bulk RNA-seq, WGS/WES) to generate data package in
support of target ID/validation
- Assist in designing experimental studies and contribute innovative ideas for improving
workflow efficiency.
Role Requirements
- PhD in Computational Biology, Genomics, Bioinformatics, Immunology, or a related field
- Experience in analyzing single cell genomics data (scRNA-seq, scATAC-seq, spatial omics)
- Strong proficiency with R and/or Python for statistical computing and data visualization
- Familiarity with popular single cell analysis tools (e.g., Seurat, Scanpy, Cell Ranger, Loupe,
etc.)
- Demonstrated ability to work independently on complex data analysis problems and
communicate results clearly to interdisciplinary teams
Preferred Qualifications
- Deep understanding of immune cell biology and ability to interpret immune-related
transcriptional signatures
- Prior experience in analysis of clinical datasets
- Basic understanding of machine learning approaches (LLMs etc.)
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