Senior Manager R&D Quality Analytics & Portfolio Insights

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Manager R&D Quality Analytics & Portfolio Insights based in the United States.

This role offers the opportunity to transform healthcare research through advanced analytics, technology, and data-driven quality oversight.
The Senior Manager will apply statistical methods, automation, and emerging technologies to identify risks and generate actionable insights across regulated R&D activities.
You will help strengthen clinical development, patient safety, data integrity, and compliance by translating complex information into strategic recommendations.
The position combines expertise in quality, life sciences, analytics, and technology to support smarter decision-making across global research programs.
You will collaborate with multidisciplinary teams and influence how organizations use data to improve quality management and operational excellence.
This is an impactful opportunity for a data-driven professional passionate about innovation, regulatory excellence, and improving outcomes for patients worldwide.

The Senior Manager R&D Quality Analytics & Portfolio Insights will develop and implement advanced analytical approaches that enhance independent quality oversight across regulated research and development activities. The role focuses on identifying emerging risks, building scalable analytics solutions, and translating complex data into meaningful quality insights that support proactive decision-making.

• Partner with R&D Quality leaders and cross-functional stakeholders to identify quality priorities, oversight opportunities, emerging risks, and areas requiring proactive intervention.
• Develop and maintain analytical frameworks, quality indicators, risk signals, thresholds, and monitoring approaches aligned with risk-based quality management and Quality by Design principles.
• Analyze clinical, safety, laboratory, monitoring, vendor, audit, operational, and quality data to identify study-level, program-level, and portfolio-level trends.
• Create statistical monitoring approaches, predictive models, anomaly detection methods, dashboards, and automated solutions to support quality oversight.
• Design and maintain analytical tools using technologies such as SQL, R, Python, Power BI, and other approved platforms.
• Translate business and quality requirements into analytical objectives, data specifications, implementation plans, and measurable outcomes.
• Perform data profiling, transformation, reconciliation, and root-cause analysis to ensure accuracy, reliability, and consistency of analytical outputs.
• Support patient safety monitoring by identifying potential risks related to adverse-event reporting, protocol compliance, data quality, and operational performance.
• Apply analytics to support GCP, GVP, GLP, and GCLP oversight activities, including audit readiness, vendor oversight, and inspection preparation.
• Establish governance practices for analytical and AI-enabled solutions, including documentation, validation, explainability, access controls, and lifecycle management.
• Create executive-level reports, visualizations, and recommendations that communicate quality risks and opportunities clearly.
• Lead analytics initiatives from initial problem definition through development, implementation, adoption, and continuous improvement.
• Collaborate with Clinical Operations, Clinical Development, Data Management, Biometrics, Pharmacovigilance, Regulatory Affairs, technology teams, and other stakeholders.
• Stay current with regulatory expectations, industry trends, statistical methods, responsible AI practices, and emerging technologies relevant to R&D quality.

The ideal candidate combines expertise in life sciences, R&D quality, analytics, and technology, with the ability to solve complex problems and communicate insights effectively across technical and business audiences.

• Experience in pharmaceutical, biotechnology, clinical research, healthcare, or another regulated life sciences environment.
• Strong knowledge of R&D Quality, clinical quality, quality assurance, compliance, risk management, or regulated research operations.
• Strong understanding of GCP and ICH requirements, with familiarity with GVP, GLP, GCLP, data integrity, and computerized system assurance.
• Experience working with structured and unstructured data, including data mapping, cleaning, transformation, reconciliation, and modeling.
• Hands-on experience applying statistical analysis, central monitoring, anomaly detection, predictive analytics, machine learning, natural language processing, or generative AI to healthcare or quality-related challenges.
• Advanced proficiency with SQL and practical experience using R or Python.
• Experience with Power BI or similar business intelligence and data visualization platforms.
• Familiarity with APIs, cloud platforms, ETL/ELT workflows, data warehouses, Git or comparable version-control systems, and…