Jobs at Bristol Myers Squibb

Lead and execute non-interventional retrospective oncology research using large datasets; develop protocols, perform statistical programming and analyses, communicate results to stakeholders, provide methodological leadership, mentor staff, and collaborate with HEOR, medical, and market access teams to support value-focused outcomes research.

Lead and execute clinical data management strategy across oncology studies, ensuring database build, validation, cleaning, and lock for NDA submissions. Manage and grow the data management team, oversee CROs and vendors, implement SOPs and automation, ensure inspection readiness, and partner cross-functionally to maintain high-quality, compliant data deliverables.

Lead global diagnostic and companion imaging radiopharmaceutical development (PET/SPECT) from candidate nomination through CMC, tech transfer, regulatory submissions, manufacturing scale-up, lifecycle management, and health authority interactions. Build and manage cross-functional teams, ensure compliance with radiopharmaceutical regulations, coordinate global regulatory strategy, manage external CDMO/CRO partners, and align imaging programs to support patient selection, dosimetry, and RLT clinical objectives.

Lead biostatistician for oncology clinical trials: design statistical strategies, author SAPs and TLFs, validate vendor deliverables, collaborate cross-functionally, support regulatory submissions, pilot AI initiatives, and provide strategic statistical guidance throughout drug development. Travel ~10%.

Coordinate, curate, validate, and maintain high-quality, reproducible research datasets for translational and clinical studies. Manage vendor/CRO data transfers, perform QC and data queries, develop data management plans and AI-ready datasets, collaborate with scientists and data engineers, and support data governance, standardization, and tooling to enable analytics and digital transformation across R&D.

Lead design, prototyping, and scaling of LLM-enabled AI capabilities for drug discovery research. Partner with scientists and engineers to build agentic systems, evaluation frameworks, grounding and provenance infrastructure, and reusable AI building blocks that improve research decision-making and scientific quality.