Principal, Clinical Data Management Lead

The Principal, Clinical Data Management Lead provides leadership and oversight for clinical data build, programming, and vendor deliverables across in-house, hybrid, and outsourced studies, ensuring alignment with CSL standards, regulatory requirements, and industry best practices. The role drives governance, review, and validation processes; leads the implementation of CDASH and SDTM standards; and oversees scalable validation frameworks and data quality controls. Additionally, it ensures effective integration of internal and external data into platforms such as the CSL Clinical Data Warehouse, while advancing automation, AI-driven solutions, and strong cross-functional collaboration with Biostatistics, Data Management, Standards, and external partners.

• Lead and develop a team of Clinical Data Validators, providing direction on clinical data standards, validation, and oversight activities across in-house, hybrid, and vendor-supported studies.
• Establish and drive clinical data governance frameworks, including review and validation processes for eCRF builds, SDTM datasets, specifications, and vendor deliverables, ensuring compliance with internal standards, regulatory requirements, and industry best practices.
• Oversee the development, implementation, and continuous improvement of clinical data standards (e.g., CDASH, SDTM) and associated validation check libraries to enable consistent, scalable, and high-quality data across studies.
• Provide strategic oversight of data integration processes, including external data flows and system interfaces, ensuring accurate, efficient, and compliant data ingestion into downstream platforms such as the Clinical Data Warehouse.
• Ensure submission readiness of clinical data by establishing robust quality control frameworks and validation processes aligned with regulatory expectations (e.g., SDTM, ADaM, define.xml).
• Develop, maintain, and govern departmental and cross-functional SOPs, standards, and QMS documentation related to clinical data oversight, standards, and programming.
• Drive cross-functional alignment by partnering with Biostatistics, Data Management, Standards, and external partners to ensure consistent understanding and application of clinical data standards and quality expectations; provide guidance, knowledge sharing, and targeted training to support effective implementation across studies.
• Provide oversight and guidance on data storage, archival, and retrieval strategies to support regulatory submissions, pooled analyses, and reuse of legacy data in collaboration with Biostatistics and other stakeholders.
• Oversee programming and validation support to ensure availability of high-quality, analysis-ready datasets, while promoting automation, efficiency, and innovation through the use of SAS and emerging technologies (e.g., Python, AI-driven solutions).