Specialist, Clinical Data Reporting

Role & responsibilities

• Program and maintain standardized clinical data reports and listings used for participant-level data review, data cleaning, and operational oversight
• Translate study reporting requirements into technical specifications and implement outputs using established frameworks and data standards
• Build and validate data review listings, exception reports, reconciliation outputs between EDC and external data, and operational performance reports
• You communicate clearly with cross-functional partners to clarify reporting needs and deliver outputs on schedule
• Partner with clinical data management and technology teams to leverage data sources including the Scientific Data Lake, EDC systems, and external data streams
• Monitor routine data refreshes, troubleshoot issues with accuracy, completeness, and integration, and contribute to automation and workflow streamlining
• Keep reporting documentation, specifications, and outputs audit- and inspection-ready in line with GCP, ICH, GDPR, SOPs, and internal quality standards
• You stay curious about reporting tools and methodologies, sharing knowledge of standards and processes to support team learning and continuous improvement

Preferred candidate profile

• Bachelor's or Master's degree in Data Science, Computer Science, Life Sciences, or a related field
• 2+ years of experience in clinical data, data visualization, or related disciplines, with a background in building interactive reporting solutions, KPIs, and metrics to support clinical trial oversight
• Proficiency in EDC systems (e.g., Medidata RAVE) and analytics, reporting, and data visualization tools such as SAS, R, Python, JReview, elluminate, Tableau, Power BI, or Spotfire
• Strong understanding of clinical trial processes, clinical data standards (e.g., CDISC), and regulatory requirements (GCP, GDPR, HIPAA)
• Experience with risk-based data review concepts, data quality standards, and data lifecycle management in a regulated environment
• Experience with data lakes, data pipelines, data integration, automation, and reporting and analytics in a clinical research environment