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Sr.Director, Clinical Data Management, Oncology

Job in Princeton, Mercer County, New Jersey, 08543, USA
Listing for: Bristol-Myers Squibb Company
Full Time position
Listed on 2026-10-20
Job specializations:
  • IT/Tech
    Data Engineering, Data Scientist, AI Engineer (Applied/Software), Data Analyst
Salary/Wage Range or Industry Benchmark: 235000 - 284000 USD Yearly USD 235000.00 284000.00 YEAR
Job Description & How to Apply Below

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits.

This is work that transforms the lives of patients, and the careers of those who do it.

Job Title

Senior Director, Clinical Data Management, Oncology

Division

Research and Development (R&D), Data and Quantitative Science (DQS), DD Data and Agentic Operations

Functional Area

Description

Data and Agentic operations team is responsible for the acquisition of clinical trial data and for ensuring the completeness and integrity of the acquired data for trials across the BMS R&D portfolio. The function is advancing toward a data-driven, automated, and AI-enabled operating model that delivers submission-quality data faster and with less burden on sites and patients.

Position Summary

The Senior Director, Clinical Data Management (CDM), Oncology is a senior leadership role providing strategic and operational leadership of the CDM function within Data and Agentic operations for the oncology portfolio. The role plans and coordinates the timely delivery of complete, high-quality, and reliable clinical trial data used for internal decision-making, regulatory approval, and market acceptance.

The role also leads the modernization of CDM into a technology-enabled, risk-based function, applying automation, advanced analytics, and responsibly governed AI/ML to improve data quality, shorten cycle times, and reduce site and patient burden, while maintaining compliance with GCP, ICH E6(R3), and evolving health authority expectations for AI in drug development.

This is a full-time office-based position with the requirement to be in office 50% of the time over 2 weeks.

Position Responsibilities Strategy and Portfolio Leadership
  • Define the strategic vision, multi-year roadmap, and operating model for the oncology CDM organization, including the innovative solutions required to create an industry-leading CDM capability.
  • Provide CDM leadership for diverse oncology programs and tumor types within the CDM organization, with oversight and governance of data deliverables for high-value and registrational clinical trials.
  • Anticipate and shape data acquisition strategy for evolving oncology trial designs and data types, such as adaptive and master-protocol studies, tumor response and imaging data, biomarker and genomic data, patient‑reported outcomes, and real‑world data.
  • Build strong, productive partnerships with key stakeholders across BMS (Clinical Development, Biostatistics and Programming, Clinical Operations, Biomarkers, Regulatory, Quality, Digital/IT) and think strategically across R&D.
AI, Automation, and Data Innovation
  • Support and execute the AI and automation strategy for CDM, identifying and prioritizing high‑value use cases such as ML‑assisted and centralized data review, anomaly and outlier detection, automated query generation and management, medical coding, CRF/edit‑check and data‑transfer specification support, external data reconciliation, and standards mapping support.
  • Lead the shift from retrospective, manual data cleaning to continuous, risk‑based, near real‑time data review and surveillance, in partnership with Clinical Operations (RBQM) and Biostatistics.
  • Ensure responsible and compliant use of AI, including generative AI and large language models, with attention to patient privacy, data confidentiality, data integrity and explainability.
  • Drive adoption…
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