Senior Manager R&D Quality Analytics & Portfolio Insights

BeiGene

10d•$136,500 - $181,500

About The Position

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.

Requirements

Experience in pharmaceutical, biotechnology, clinical research, healthcare, or another regulated life-sciences environment, with demonstrated knowledge of R&D Quality, clinical quality, quality assurance, compliance, risk management, or regulated research and development operations.
Strong working knowledge of GCP and ICH requirements, with familiarity or experience in GVP, GLP, GCLP, data integrity, computerized-system assurance, and global R&D Quality expectations.
Experience with structured and unstructured data, data mapping, data cleaning, reconciliation, transformation, data modeling, automated quality checks, and data pipelines.
Experience applying statistical analysis, central statistical monitoring, anomaly detection, forecasting, predictive modeling, machine learning, natural-language processing, generative AI, or related methods to practical clinical, safety, research, or quality problems.
Ability to understand complex R&D Quality and operational processes and translate them into scalable technical solutions without losing regulatory context, scientific relevance, or business usability.
Ability to communicate technical findings clearly to nontechnical stakeholders and convert complex analyses into concise, actionable quality recommendations.
Advanced proficiency with SQL and hands-on proficiency with R or Python.
Experience with Power BI or comparable visualization and business-intelligence platforms.
Experience with statistical programming, central monitoring, anomaly detection, predictive modeling, or machine-learning tools and methods.
Experience with APIs, data warehouses, cloud platforms, ETL/ELT workflows, Git or comparable version-control tools, and analytical development environments.
Proficiency with Microsoft Office applications; experience with clinical, safety, laboratory, RBQM, audit, and quality platforms is preferred.
Excellent written, verbal, presentation, and interpersonal communication skills.
Ability to collaborate effectively across global, cross-functional, clinical, safety, research, quality, data, and technical teams.
Ability to work with sensitive or confidential clinical, safety, quality, laboratory, and patient-level information in accordance with applicable requirements.

Nice To Haves

Experience with automation, natural-language processing, generative AI, or model deployment and monitoring tools is preferred.

Responsibilities

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 intended use, data lineage, testing, validation or assurance, access controls, version control, change management, human review, explainability, performance monitoring, and retirement planning proportionate to risk.
Maintain appropriate technical and quality documentation to support reproducibility, traceability, inspection readiness, and continued fitness for purpose in a regulated R&D environment.
Create executive-ready visualizations, quality narratives, risk summaries, and recommendations that explain what the data means, why it matters, and what action should be considered.
Support Quality Management Review, portfolio quality surveillance, RBQM governance, audit planning, inspection readiness, vendor oversight, computerized-system oversight, and continuous improvement through timely and relevant analytical insights.
Partner with Clinical Operations, Clinical Development, Data Management, Biometrics, Medical Monitoring, Pharmacovigilance, Regulatory Affairs, laboratory functions, RBQM, Audit and Inspection Management, Global Technology Solutions, data engineering, and enterprise platform teams.
Lead or coordinate analytics workstreams from problem framing and data assessment through development, testing, implementation, adoption, monitoring, and benefit realization.
Stay current with global R&D regulatory expectations, industry practices, statistical methods, responsible AI principles, and emerging technologies relevant to GCP, GVP, GLP, and GCLP Quality oversight.