Senior Manager R&D Quality Analytics & Portfolio Insights

BeOne continues to grow at a rapid pace with challenging and exciting opportunities for experienced professionals. When considering candidates, we look for scientific and business professionals who are highly motivated, collaborative, and most importantly, share our passionate interest in fighting cancer.

The Senior Manager R&D Quality Analytics & Portfolio Insights is a specialized R&D Quality role focused on applying advanced analytics, statistical methods, and technology-enabled approaches to strengthen independent Quality oversight of clinical development, participant protection, patient safety, and regulated research activities. This position requires practical expertise in clinical and patient-safety Quality and uses data as a means to identify emerging risks, generate actionable quality insights, and support timely, evidence-based decisions by accountable GCP, GVP, GLP, and GCLP Quality Leads.

The role develops, implements, and maintains fit-for-purpose analytical methods and independent oversight-support tools tailored to regulated R&D activities. These may include statistical monitoring, predictive risk models, anomaly-detection methods, patient-safety and adverse-event reporting analytics, data-informed audit packages, risk indicators, dashboards, automated monitoring, natural-language tools, and other scalable capabilities that support Quality by Design, Risk-Based Quality Management, audit and inspection readiness, vendor, process, and system oversight, and targeted risk mitigation.

The position integrates and interprets complex clinical, safety, laboratory, monitoring, operational, and quality data to identify study-, site-, subject-, vendor-, process-, product-, and portfolio-level signals that may affect participant protection, patient safety, data integrity, scientific reliability, or regulatory compliance. The role translates these signals into clear insights and recommendations for R&D Quality colleagues and cross-functional partners.

• Partner with R&D Quality leadership and GCP, GVP, GLP, and GCLP stakeholders to identify priority quality questions, emerging risks, oversight gaps, and opportunities for proactive intervention.
• Apply quality, regulatory, scientific, and operational context to ensure analytical methods remain relevant to participant protection, patient safety, data integrity, scientific validity, and the reliability of regulated R&D activities.
• Conduct cross-study, cross-program, cross-vendor, and cross-process analyses to identify recurring, systemic, or portfolio-level risks that may not be visible through individual study or functional review.
• Develop and maintain meaningful key quality indicators, critical-to-quality factors, quality tolerance limits, risk indicators, thresholds, escalation criteria, and portfolio surveillance methods aligned with RBQM and Quality by Design principles.

• Integrate and analyze relevant clinical study, site, subject, safety, pharmacovigilance, laboratory, monitoring, audit, inspection vendor, system, and quality data to support R&D Quality oversight.
• Develop analytical approaches to support data-informed audit planning/execution and identify potential patient-safety and subject-protection risks, including adverse-event under-reporting or over-reporting, unusual clinical-event patterns, protocol-compliance concerns, data-quality issues, and other signals requiring targeted quality review.
• Support GLP and GCLP oversight by developing analytics that identify trends and anomalies in research, laboratory, biomarker, sample, method, data-flow, and computerized-system activities, as appropriate to the intended use and available data.
• Support GVP oversight through analytics related to safety reporting, case processing, signal-relevant quality indicators, vendor performance, timeliness, completeness, and other patient-safety process risks.

• Translate R&D Quality and business needs into clear analytical objectives, data requirements, functional specifications, acceptance criteria, implementation plans, and measurable outcomes.
• Design, develop, test, implement, and maintain statistical packages, analytical applications, dashboards, automated workflows, predictive models, and decision-support tools using SQL, R, Python, Power BI, and other approved technologies.
• Use automation, machine learning, natural-language processing, generative AI, or other emerging methods where they provide a justified, fit-for-purpose improvement

• Perform data profiling, mapping, cleaning, transformation, source-to-report reconciliation, and root-cause analysis to confirm the accuracy, completeness, consistency, timeliness, and reliability of analytical outputs.
• Ensure analytical and AI-enabled solutions are appropriately governed through documented intend