Senior Manager R&D Quality Analytics & Portfolio Insights

In this role you will apply advanced analytics to strengthen independent quality oversight of regulated R&D activities. You drive data-informed insights to protect participants and support compliant decisions across GCP, GVP, GLP, and GCLP. You will develop scalable analytics and tools that enable risk monitoring, quality indicators, and portfolio surveillance. This role partners across R&D Quality and cross-functional teams to translate signals into actionable recommendations that advance patient safety and data integrity.

• Identify priority quality questions and emerging risks with leadership and stakeholders
• Develop and maintain quality indicators, risk indicators, thresholds, and portfolio surveillance methods aligned with RBQM and QbD
• Integrate clinical, safety, laboratory, monitoring, vendor, and quality data to support oversight
• Design and maintain analytical products: statistical packages, dashboards, predictive models, and decision-support tools
• Support audit planning, inspection readiness, and risk-based quality management through analytics
• Translate business needs into analytical objectives, data requirements, and implementation plans
• Lead or coordinate analytics workstreams from problem framing to adoption and benefit realization
• Stay current with global regulatory expectations and emerging analytics technologies
• Ensure governance, data lineage, validation, and explainability for AI-enabled solutions
• Collaborate across clinical operations, data management, pharmacovigilance, regulatory affairs, and IT teams
• Provide executive-ready narratives and visuals explaining data insights and actions
• Travel up to 20% as needed

• Experience in pharma/biotech/regulated life sciences with knowledge of R&D quality and regulatory requirements
• Strong knowledge of GCP and ICH; familiarity with GVP, GLP, GCLP, data integrity, and quality expectations
• Experience with structured and unstructured data, data cleaning, mapping, reconciliation, transformation, and data pipelines
• Experience applying statistics, central monitoring, anomaly detection, predictive modeling, ML, NLP, or related methods to clinical/quality problems
• Ability to translate complex R&D quality processes into scalable technical solutions without losing regulatory context
• Strong communication skills to present technical findings to nontechnical stakeholders

• collaborative
• communication
• problem-solving
• SQL
• R
• Python