Senior Manager, Clinical Data Management

Responsibilities

• Serve as the lead data manager for assigned clinical studies and programs
• Develop and maintain study-specific data management strategies, timelines, and deliverables
• Review and approve key data management documents including Data Management Plans and eCRF specifications
• Provide hands-on oversight of Medidata Rave EDC database builds
• Plan, perform, and document testing of database builds and system functionality
• Lead data review, query management, external data reconciliation, and database lock activities
• Partner with Clinical Operations, Biostatistics, and Medical Monitoring teams
• Monitor data quality and support inspection readiness
• Review CRO financial reports and associated data management expenses
• Contribute to continuous improvement of data management processes and standards

Requirements

• Bachelor's degree in Life Sciences, Health Sciences, Computer Science, or a related discipline
• At least 7+ years of clinical data management experience in pharmaceutical, biotech, or CRO industry
• Required hands-on experience with Medidata Rave EDC, eCOA and RTSM
• Demonstrated experience creating, running, reviewing, and interpreting Medidata Rave reports and listings
• Demonstrated experience testing clinical database builds, UAT scripts, and eCRFs
• Solid understanding of database design, query management, and database lock processes
• Strong knowledge of ICH-GCP and applicable regulatory requirements
• Excellent written, verbal, and interpersonal communication skills
• Strong organizational and prioritization skills

Preferred Qualifications

• Experience with Spotfire or similar data visualization and analytics tools
• Experience working in a small to mid-sized biotechnology company environment
• Familiarity with CDISC standards, risk-based data review methodologies, and external vendor data integrations

About the Company

Generate Biomedicines is a clinical-stage generative biology company pioneering the AI revolution in drug design and development, integrating machine learning with large-scale experimentation to design proteins with defined biological intent.