Intern, Bioinformatics at Revolution… · San…

Summary: Revolution Medicines is a clinical-stage precision oncology company focused on developing novel targeted therapies for RAS-addicted cancers. They are seeking a highly motivated Bioinformatics Intern to support the analysis of single-cell datasets related to KRAS inhibitors, working closely with computational biologists and translational scientists to generate insights for therapeutic strategies. Responsibilities: - Integrate multiple single-cell RNA-seq (scRNA-seq) across studies and platforms - Perform data preprocessing, quality control, normalization, and batch correction - Harmonize metadata and cell annotations across datasets - Conduct clustering, cell-type identification, and differential expression analyses - Characterize tumor microenvironment changes following KRAS inhibitor treatment, including immune infiltration shifts, myeloid reprogramming, T cell activation/exhaustion states, and stromal remodeling - Perform pathway enrichment and gene signature analyses - Generate clear visualizations - Develop reproducible, well-documented computational workflows - Present findings to cross-functional research teams Required Qualifications: - Proficiency in Python (preferred) or R for data analysis - Experience working with high-dimensional biological datasets - Familiarity with machine learning or statistical modeling - Strong data wrangling and preprocessing skills - Understanding of molecular biology and genomics concepts - Ability to write clean, reproducible, and well-documented code - Strong analytical thinking and problem-solving skills - Effective written and verbal communication skills Preferred Qualifications: - Experience with single-cell analysis frameworks (e.g., Scanpy, Seurat, AnnData) - Familiarity with batch correction and dataset integration methods (e.g., Harmony, scVI, Seurat integration) - Experience analyzing RNA-seq, ATAC-seq, or other sequencing-based assays - Knowledge of tumor immunology or tumor microenvironment biology - Experience with pathway enrichment or gene set analysis tools - Prior experience in cancer genomics or translational research - Experience working in a collaborative research environment Required Skills: Python, Single-cell RNA-seq, Data preprocessing Important Skills: Machine learning, Molecular biology, Data wrangling Nice-to-Have Skills: Analytical thinking, Collaborative research, Problem-solving, Effective communication Benefits: Competitive cash compensation, Robust equity awards, Strong benefits, Significant learning and development opportunities