Tech Stack

Description

Lead the development of advanced machine learning approaches to accelerate small-molecule drug discovery at a late-stage clinical oncology company. You will work at the intersection of data science, chemistry, and biology, transforming complex datasets into predictive models that guide target discovery, compound design, and translational hypotheses.

Requirements

• PhD in ML, computational chemistry, computational biology, CS, or related quantitative discipline
• 8+ years applying ML or advanced analytics to scientific problems
• Experience with chemical or biological datasets in drug discovery
• Expertise in PyTorch, TensorFlow, scikit-learn, NumPy, Pandas, deep learning
• Strong understanding of early-stage drug discovery workflows

Responsibilities

• Lead ML strategies to accelerate early-stage drug discovery
• Develop predictive models for compound activity, selectivity, ADME/Tox, and developability
• Apply modern ML techniques: GNNs, deep learning for molecular representation, generative chemistry, active learning
• Partner with medicinal chemists and biologists to guide compound design and interpret experimental data
• Integrate heterogeneous datasets including chemical, screening, imaging, and structural biology data