(data scientist)

The RBQM Data Scientist supports central monitoring and risk-based quality management (RBQM) for clinical trials. This role focuses on implementing and running pre-defined KRIs, QTLs, and other risk metrics using clinical data, with strong emphasis on SAS programming to deliver robust and scalable analytics across multiple studies.


KEY RESPONSIBILITIES:
The RBQM Data Scientist may perform a range of the following responsibilities, depending upon the studies' complexity and studies' development stage:
• Implement and maintain pre-defined KRIs, QTLs, and triggers using robust SAS programs/macros across multiple clinical studies.
• Extract, transform, and integrate data from EDC systems (e.g., RAVE) and other clinical sources into analysis-ready SAS datasets.
• Run routine and ad-hoc RBQM/central monitoring outputs (tables, listings, data extracts, dashboard feeds) to support signal detection and study review.
• Perform QC and troubleshooting of SAS code; ensure outputs are accurate and efficient.
• Maintain clear technical documentation (specifications, validation records, change logs) for all RBQM programs and processes.
• Collaborate with Central Monitors, Central Statistical Monitors, Data Management, Biostatistics, and Study Operations to understand requirements and ensure correct implementation of RBQM metrics.


Qualifications
Education & Experience
• PhD, MS, or BA/BS in statistics, biostatistics, computer science, data science, life science, or a related field.
• Relevant clinical development experience (programming, RBM/RBQM, Data Management), for example:
o PhD: 3+ years
o MS: 5+ years
o BA/BS: 8+ years


Technical - Required
• Advanced SAS programming skills (hard requirement) in a clinical trials environment (Base SAS, Macro, SAS SQL; experience with large, complex clinical datasets).
• Hands-on experience working with clinical trial data.
• Proficiency with Microsoft Word, Excel, and PowerPoint.
Technical - Preferred / Strong Plus
• Experience with RAVE EDC.
• Awareness or working knowledge of CDISC, CDASH, SDTM standards.
• Exposure to R, Python, or JavaScript and/or clinical data visualization tools/platforms.