Machine Learning Scientist II, Drug Discovery Analytics
Core
Develop and apply predictive models and analytical methods to transform complex biological and chemical datasets into actionable insights for drug discovery research teams.
Role type
Machine Learning Scientist II (Drug Discovery)
Builds
Predictive models for compound activity, selectivity, developability, and phenotypic screening outcomes
Domain
Biotechnology / Pharmaceutical / Drug Discovery
Deliverable
production ML models
Required skills
Python, PyTorch, TensorFlow, scikit-learn, supervised learning, deep learning, graph-based methods, ensemble methods, data visualization, exploratory data analysis
Preferred skills
Phenotypic screening, high-content imaging, Cell Painting, representation learning, cheminformatics, RDKit, OpenEye, multi-omics analysis, MLOps
Responsibilities
Develop and evaluate machine-learning models for drug discovery questions; Perform exploratory data analysis on chemical, biological, and imaging datasets; Prepare and integrate heterogeneous datasets; Apply modeling approaches under guidance; Validate model performance and robustness; Collaborate with engineering partners for reproducible workflows; Partner with scientists to translate questions into analyses
Seniority
Mid-level Individual Contributor
