Computational Scientist

1 week ago

San Francisco, California, United States BayOne Solutions Full time

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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