Principal Machine Learning Scientist, Drug Discovery Analytics

Key Responsibilities

• Scientific Leadership: Define and lead machine learning strategies that accelerate early-stage drug discovery.
• Identify opportunities where AI and advanced analytics can meaningfully improve scientific decision-making.
• Drive the adoption of innovative modeling approaches within multidisciplinary discovery teams.
• Model Development: Develop predictive models for:
  • Compound activity, selectivity, ADME/Tox, and developability properties.
  • Target engagement, mechanism-of-action, and phenotypic datasets.
• Apply modern ML techniques such as: Graph neural networks, Deep learning for molecular representation, Generative chemistry models, Active learning frameworks for experimental design.

Required Skills, Experience and Education

• PhD in machine learning, computational chemistry, computational biology, computer science, or a related quantitative discipline.
• 8+ years experience applying machine learning or advanced analytics to scientific problems.
• Demonstrated experience working with chemical or biological datasets in drug discovery or related domains.
• Strong expertise in: Python-based ML ecosystems (PyTorch, TensorFlow, scikit-learn).
• Data analysis and scientific computing (NumPy, Pandas).
• Deep learning and representation learning techniques.
• Strong understanding of early-stage drug discovery workflows.
• Ability to translate biological or chemical questions into computational frameworks and predictive models.
• Proven ability to communicate complex computational insights to.
• Passion for scientific innovation and a relentless commitment to improving patient outcomes.

Preferred Skills

• Proven track record of applying advanced AI/ML approaches (deep learning, generative modeling, structure-based ML) to drug discovery or related life sciences domains.
• Experience with cheminformatics or bioinformatics toolkits is highly desirable.
• Familiarity with cloud computing and scalable ML workflows is a plus.
• Ability to work at the interface of computational and experimental science.

Base Pay Salary Range