Intern, Computational Oncology

Revolution Medicines is a clinical-stage precision oncology company focused on developing novel targeted therapies to inhibit frontier targets in RAS-addicted cancers.  The company’s R&D pipeline comprises RAS(ON) Inhibitors designed to suppress diverse oncogenic variants of RAS proteins, and RAS Companion Inhibitors for use in combination treatment strategies. As a new member of the Revolution Medicines team, you will join other outstanding Revolutionaries in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway.

The Opportunity:

Revolution Medicines is seeking a motivated summer intern to evaluate computational strategies for resolving tumor and tumor microenvironment (TME) states from bulk, single-cell, and spatial transcriptomic data in oncology. This project will focus on benchmarking deconvolution methods and assessing single-cell analysis best practices to determine the most biologically accurate and reproducible approaches for studying treatment response and resistance to targeted therapies, including RAS(ON) inhibitors.

• Evaluate Deconvolution Methods:

• Review leading bulk and spatial deconvolution tools.

• Benchmark selected methods using curated datasets and single-cell references.

• Assess robustness in detecting immune shifts, resistant tumor states, and TME remodeling.

• Test Single-Cell Analysis Best Practices:

• Compare normalization, integration, and batch correction strategies.

• Evaluate clustering robustness and annotation reproducibility.

• Assess the impact of different processing steps on biological interpretation.

Required Skills, Experience and Education:

• Pursuing a BS or MS in Computational Biology, Bioinformatics, Systems Biology, or related field.

• Proficiency in R and Python.

Preferred Skills:

• Experience analyzing RNA-seq data (bulk and/or single-cell).

• Experience with Seurat, Scanpy, or scVI.

• Familiarity with tumor microenvironment biology. 

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