Tech Stack

Description

You will develop machine learning models and analytical methods to transform complex biological and chemical datasets into actionable insights, accelerating drug discovery for RAS-addicted cancers. Collaborate closely with chemists and biologists to support target discovery, compound optimization, and translational research.

Requirements

• PhD in machine learning, computational biology, computational chemistry, computer science, statistics, or related quantitative field
• 4-8 years experience applying ML or advanced analytics to scientific datasets
• Proficiency in Python, NumPy, Pandas, SciPy, PyTorch, TensorFlow, scikit-learn
• Experience with model development, validation, and evaluation methods
• Experience with noisy and incomplete experimental datasets

Responsibilities

• Design and implement ML models for compound activity, selectivity, and developability
• Develop predictive frameworks for ADME/Tox, target engagement, and phenotypic screening
• Perform exploratory data analysis on chemical, biological, and phenotypic datasets
• Integrate heterogeneous datasets including chemical structure, screening, and imaging data
• Collaborate with medicinal chemists and biologists on compound design and target identification