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.
