Machine Learning Scientist II, Drug Discovery Analytics

Role: Machine Learning Scientist II supporting drug discovery at Revolution Medicines, focusing on advanced analytics and AI to accelerate targeting and compound optimization for RAS-addicted cancers. Responsibilities: develop, evaluate, and implement predictive models across biological, chemical, and imaging datasets; perform exploratory data analysis; collaborate with chemists, biologists, and data engineers to integrate models into discovery workflows; document methods for reproducibility. Requirements: Ph.D. or M.S. with relevant experience in machine learning, computational biology/chemistry, or related fields; 2-5 years applying ML to scientific datasets; strong Python skills; experience with frameworks like PyTorch, TensorFlow, scikit-learn; capable of working with noisy experimental data. Preferred: biotech/pharma experience, familiarity with phenotypic screening, cheminformatics tools like RDKit, and multi-omics data analysis. High-Value: focus on oncology (RAS-driven cancers), drug discovery, predictive modeling, imaging, phenotypic profiling, and integration with biological interpretation. Work setup: not specified.