Machine Learning Scientist II, Drug Discovery Analytics

Revolution Medicines•Redwood City, CA

About The Position

Revolution Medicines is seeking a Machine Learning Scientist II to accelerate drug discovery through advanced analytics and artificial intelligence. This hands-on individual contributor will develop and apply predictive models and analytical methods that transform complex biological and chemical datasets into actionable insights for research teams. The role works at the interface of data science, chemistry, and biology, supporting target discovery, compound optimization, phenotypic screening, and translational research. Working with senior data scientists and experimental collaborators, the scientist will contribute analyses, models, and reusable workflows to a data-driven discovery ecosystem where data, analytics, and experimentation inform one another.

Requirements

Ph.D. in machine learning, computational biology, computational chemistry, computer science, statistics, bioinformatics, or a related quantitative field; alternatively, an M.S. degree with relevant industry experience.
Typically 2–5 years of relevant experience applying machine learning, data science, or advanced analytics to scientific datasets; relevant doctoral research may be considered.
Demonstrated experience developing, validating, and evaluating predictive or classification models.
Strong Python programming skills and experience with scientific computing libraries such as NumPy, Pandas, and SciPy.
Hands-on familiarity with machine-learning frameworks such as PyTorch, TensorFlow, and/or scikit-learn.
Experience with data visualization, exploratory data analysis, and working with noisy or incomplete experimental datasets.
Ability to communicate technical work clearly and collaborate effectively with cross-functional scientific partners.

Nice To Haves

Experience in biotechnology, pharmaceutical, healthcare, or drug discovery environments.
Experience with phenotypic screening, high-content imaging, Cell Painting, morphological profiling, or computer vision for microscopy images.
Familiarity with representation learning, self-supervised learning, embedding generation, dimensionality reduction, clustering, or phenotype discovery.
Familiarity with cheminformatics or molecular modeling tools, such as RDKit or OpenEye.
Experience with multi-omics data analysis, cloud computing environments, MLOps, or scalable model deployment.
Working knowledge of cell biology, drug discovery workflows, assay development, microscopy, experimental design, or biological interpretation of machine-learning results.

Responsibilities

Develop, implement, and evaluate machine-learning models for drug discovery questions, including compound activity, selectivity, developability, target engagement, and phenotypic screening outcomes.
Perform exploratory data analysis and quality assessment on chemical, biological, imaging, and phenotypic datasets.
Prepare and integrate heterogeneous datasets, including chemical structure and screening data, structural biology outputs, molecular simulation outputs, and high-content imaging or morphological profiling data.
Apply appropriate modeling approaches, including supervised learning, deep learning, graph-based methods, and ensemble methods, under the guidance of project and functional leads.
Use sound validation strategies to assess model performance, robustness, applicability, and limitations.
Collaborate with data engineering and machine-learning engineering partners to support reproducible workflows and integrate analytical outputs into discovery pipelines.
Partner with medicinal chemists, biologists, and other research scientists to translate scientific questions into computational analyses and communicate results clearly.
Document methods, code, results, and key assumptions to support reproducibility and knowledge sharing.