Overview Join Revolution Medicines to accelerate drug discovery with advanced analytics. You will build predictive models that translate complex biological and chemical data into actionable insights, bridging data science with chemistry and biology. Your work supports target discovery, lead optimization, and translational research in a cross-disciplinary team. You will help create a data-driven discovery ecosystem that speeds scientific decisions and improves patient outcomes. This role offers the chance to shape RAS-targeted programs and collaborate with world-class scientists.
Compensation / Benefits- base pay range $251,000-$295,000 USD
- equity awards
- robust benefits
- learning and development opportunities
Responsibilities- Design and implement ML models to predict compound activity, selectivity, and developability
- Develop frameworks for ADME/Tox, target engagement, and phenotypic screening outcomes
- Leverage deep learning, graph neural networks, and ensemble methods
- Validate models and establish robust evaluation strategies
- Collaborate with data engineers and ML engineers to embed models into discovery pipelines
- Perform exploratory data analysis on chemical, biological, and phenotypic datasets
- Integrate heterogeneous data sources including chemical structures and structural biology/molecular simulations outputs
- Partner with medicinal chemists and biologists to translate questions into modeling strategies
Key requirements- PhD in a quantitative field (machine learning, computational biology/chemistry, computer science, statistics)
- 6-10 years applying ML/advanced analytics to scientific data
- Proficiency in Python and libraries (NumPy, Pandas, SciPy)
- Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn)
- Model development, validation, and evaluation expertise
- Data visualization and exploratory analysis skills
- Experience with noisy and incomplete experimental datasets
- collaboration with experimental scientists
- strong communication of complex results
- problem-solving and cross-functional teamwork
- Cheminformatics or molecular modeling tools (RDKit, OpenEye)
- Multi-omics data analysis
- Cloud computing environments
