Senior Machine Learning Scientist II, Drug Discovery Analytics

Revolution 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 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 rigorall 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 Discovery

• Independently 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.

• 610 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.

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