Senior Machine Learning Scientist I, Drug Discovery Analytics

Key Responsibilities

• 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.
• 6–10 years of 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.