Senior Machine Learning Scientist I, Drug Discovery Analytics

Senior Machine Learning Scientist I, Drug Discovery AnalyticsRevolution Medicines is a global, commercial-state oncology company dedicated to discovering, developing and delivering innovative medicines for patients with RAS-addicted cancers. Leveraging its differentiated RAS(ON) tri-complex inhibitor platform, the company is advancing a broad, integrated portfolio of oral RAS(ON) inhibitors designed to directly target the active, cancer-driving state of RAS. Founded on rigorous scientific inquiry and a willingness to challenge long-held assumptions, Revolution Medicines is committed to changing the trajectory of disease for patients with RAS-addicted cancers worldwide.Our people are united by a shared way of working: follow the science, challenge assumptions, act with urgency and hold ourselves to a high standard of rigor—all in service of patients.The Opportunity:We are seeking a Senior Machine Learning Scientist to help accelerate drug discovery through advanced analytics and artificial intelligence. This role will develop predictive models and analytical methods that transform complex biological and chemical datasets into actionable insights that guide research decisions.The Senior Machine Learning Scientist will work at the interface of data science, chemistry, and biology to support target discovery, compound optimization, and translational research. This position requires both strong machine learning expertise and the ability to collaborate effectively with experimental scientists to solve real-world scientific problems.The successful candidate will contribute to building a data-driven discovery ecosystem where data, analytics, and experimentation continuously inform and accelerate one another.Key responsibilities include:Develop Predictive Models for Drug DiscoveryIndependently Design and implement machine learning models to predict compound activity, selectivity, and developability.Identify and Develop predictive frameworks for ADME/Tox, target engagement, and phenotypic screening outcomes.Apply advanced modeling approaches including deep learning, graph neural networks, and ensemble methods.Evaluate model performance and apply appropriate validation strategies.Work with data engineers and ML engineers to integrate models into discovery pipelines.Analyze Complex Scientific Data.Perform exploratory data analysis on chemical, biological, and phenotypic datasets.Integrate heterogeneous datasets including:Chemical structure and screening data.Structural biology and molecular simulation outputs.Collaborate with Research Scientists.Partner with medicinal chemists to support compound design and lead optimization.Work with biologists to interpret experimental results and identify new target opportunities.Translate scientific questions into computational modeling strategies.Required Skills, Experience and Education:PhD in machine learning, computational biology, computational chemistry, computer science, statistics, or a related quantitative field.7-9 years experience applying machine learning or advanced analytics to scientific datasets.Python and scientific computing libraries (NumPy, Pandas, SciPy).Machine learning frameworks (PyTorch, TensorFlow, scikit-learn).Model development, validation, and evaluation methods.Data visualization and exploratory analysis.Experience working with noisy and incomplete experimental datasets.Preferred Skills:Cheminformatics or molecular modeling tools (RDKit, OpenEye, etc.).Multi-omics data analysis.Cloud computing environments.MLOps or scalable model deployment.Base Pay Salary Range $229,000 - $269,000 USD