About this role

Lead the scientific development of cutting-edge agentic frameworks for oncology
Architect sophisticated "deep agents" for hypothesis generation, experimental design, and multimodal ML and AI modeling
Innovate scientific methodology by developing new predictive models and causal inference frameworks
Work with Research, Engineering & Data Science teams to develop innovative computational solutions
Collaborate with clinical scientists and pharma partners to define high-value use cases

Minimum PhD (or Masters degree with 5+ years of relevant experience)
2+ years of relevant industry or post-doctoral experience involving medicine and AI
Quantitative and computational skills in AI agent based workflows (e.g. Applied Machine Learning, Generative AI, Mathematics, biostatistics)
Biological, medical, translational or drug development knowledge and data (e.g. oncology, RWE, medical science, or clinical drug development)
Expert-level proficiency in Python and orchestration frameworks, specifically LangGraph or similar
Experience building deep agents with complex state management and graphs
Deep knowledge of prompt engineering, RAG (Retrieval-Augmented Generation), function calling, and evaluating non-deterministic LLM outputs
Strong foundation in survival analysis (CoxPH, RSF, NN) and evaluation metrics for oncology models
Experience working with clinical trial or real-world data, clinical guidelines
Track record of success in peer-reviewed publications or proven impact

Senior Scientist II, Applied Machine Learning, Translational Agentic AI

Key skills