Machine Learning Scientist II

Role: Machine Learning Scientist II focused on accelerating drug discovery in oncology through advanced analytics and AI. Responsibilities include developing predictive models for compound activity, target engagement, and phenotypic screening, integrating heterogeneous datasets (chemical, biological, imaging), and collaborating with cross-disciplinary teams to translate scientific questions into computational solutions. Requirements: Ph.D. in a quantitative field or M.S. with relevant experience; 2-5 years applying machine learning to scientific datasets; proficiency in Python, ML frameworks (PyTorch, TensorFlow, scikit-learn); strong data analysis and visualization skills; effective communication and collaboration abilities. Preferred: biotech or pharma experience, familiarity with phenotypic imaging techniques, cheminformatics tools, and multi-omics data analysis. High-Value: drug discovery, biological and chemical datasets, phenotypic screening, AI/ML, cloud computing, scalable deployment, working in a biotech R&D setting. Work setup: not explicitly stated, but likely collaborative with cross-functional teams.