Associate Director, Oncology Clinical Intelligence & Applied Analytics

Role: Lead evidence generation and advanced analytics in Hematology to inform Phase 3 development, trial design, and market access strategies. Responsibilities: partner with stakeholders to identify evidence gaps, apply machine learning and causal inference techniques for patient stratification, external control arms, and prognostic modeling; develop and validate multimodal patient models integrating clinical, genomic, and real-world data; translate complex data into decision-ready insights to optimize portfolio development; collaborate with platform teams to incorporate new tools and methods; ensure analyses meet regulatory and HTA standards. Requirements: PhD or equivalent in epidemiology, biostatistics, computational biology, or related field; 5+ years in evidence strategy, real-world evidence, and advanced analytics; strong expertise in causal inference, observational study design, and machine learning; proficiency in Python, R, SQL, and cloud environments. Preferred: hematology/oncology domain knowledge, experience with external control arms, trial simulators, or biomarker integration, and a record of influencing cross-functional strategies. HighValue: hematology, Phase 3, real-world evidence, causal inference, multimodal patient models, AI analytics, regulatory standards, cloud-based platforms. WorkSetup: not specified; likely hybrid with emphasis on collaboration and data science tools.