Sr Project Manager, QMS Measurement Systems

Integra LifeSciences

About The Position

Changing lives. Building Careers. Joining us is a chance to do important work that creates change and shapes the future of healthcare. Thinking differently is what we do best. To us, change equals opportunity. Every day, our colleagues are challenging what’s possible and making headway to innovate new treatment pathways to advance patient outcomes and set new standards of care. The Digital Project Manager will be responsible for leading the design, deployment, and continuous improvement of enterprise measurement systems supporting Quality Management System (QMS) performance and operational excellence. This role bridges business, quality, analytics, and IT functions to deliver scalable, validated, and decision-ready digital solutions. Success is measured through improved data integrity, standardized global reporting, actionable trending, and demonstrable business value (e.g., efficiency, risk reduction, and decision speed). Lead the development and operationalization of enterprise measurement systems across QMS domains, including CAPAs, Nonconformances, Complaints, Audits, and other regulated quality elements. Translate end-to-end quality processes and lifecycle logic into standardized metrics, KPIs, and analytical frameworks that are auditable, traceable, and decision-relevant. Ensure consistent interpretation of quality data across sites by aligning definitions, hierarchies, and governance standards. Design, develop, and maintain advanced Power BI dashboards and applications, serving both operational and executive audiences. Build robust data models, measures, and calculated logic to support trending, benchmarking, and early-warning indicators. Automate repeatable reporting workflows and enable near real-time visibility into quality performance and risk signals. Apply statistical and analytical methods (e.g., control charts, rate-based trending, variance analysis) to monitor stability, detect signals, and support proactive decision-making. Utilize R for statistical trending, exploratory analysis, and validation of quality metrics and performance indicators. Leverage Python selectively for data preparation, exploratory analytics, or proof-of-concept predictive use cases where appropriate. Partner closely with IT, data engineering, and system owners to ensure reliable data pipelines, secure architectures, and scalable solutions. Collaborate with manufacturing sites, quality leaders, and functional SMEs to gather requirements, validate outputs, and drive adoption. Act as a liaison between technical teams and business stakeholders, ensuring solutions meet both regulatory and operational expectations. Own all phases of the digital project lifecycle, from ideation and requirements definition through deployment, adoption, and value realization. Develop and manage project plans, timelines, risks, and dependencies, providing clear status reporting to stakeholders. Apply structured change-management practices to drive user adoption, training, and sustained utilization of digital solutions. Identify high-value opportunities for process optimization, advanced analytics, and automation. Develop proofs-of-concept leveraging data science, AI, or machine-learning techniques to address emerging business needs. Lead and cultivate a user community focused on digital enablement, analytics literacy, and continuous improvement.

Requirements

Bachelor’s degree in Engineering, Computer Science, Data Analytics, or a related technical field.
5+ years of experience in digital project management, measurement systems, analytics, or performance excellence roles.
Advanced proficiency in Power BI, including data modeling, DAX, and dashboard/application design.
Working knowledge of QMS processes and regulated environments (e.g., CAPAs, NCs, Complaints).
Experience using R for statistical analysis and trending; familiarity with Python for analytics or automation is a plus.
Proven ability to partner with IT and cross-functional teams to deliver scalable, compliant digital solutions.
Strong communication skills, with the ability to influence without authority and translate technical concepts for non-technical audiences.
Demonstrated experience building ROI-driven business cases and value-based use cases.

Responsibilities

Lead the development and operationalization of enterprise measurement systems across QMS domains, including CAPAs, Nonconformances, Complaints, Audits, and other regulated quality elements.
Translate end-to-end quality processes and lifecycle logic into standardized metrics, KPIs, and analytical frameworks that are auditable, traceable, and decision-relevant.
Ensure consistent interpretation of quality data across sites by aligning definitions, hierarchies, and governance standards.
Design, develop, and maintain advanced Power BI dashboards and applications, serving both operational and executive audiences.
Build robust data models, measures, and calculated logic to support trending, benchmarking, and early-warning indicators.
Automate repeatable reporting workflows and enable near real-time visibility into quality performance and risk signals.
Apply statistical and analytical methods (e.g., control charts, rate-based trending, variance analysis) to monitor stability, detect signals, and support proactive decision-making.
Utilize R for statistical trending, exploratory analysis, and validation of quality metrics and performance indicators.
Leverage Python selectively for data preparation, exploratory analytics, or proof-of-concept predictive use cases where appropriate.
Partner closely with IT, data engineering, and system owners to ensure reliable data pipelines, secure architectures, and scalable solutions.
Collaborate with manufacturing sites, quality leaders, and functional SMEs to gather requirements, validate outputs, and drive adoption.
Act as a liaison between technical teams and business stakeholders, ensuring solutions meet both regulatory and operational expectations.
Own all phases of the digital project lifecycle, from ideation and requirements definition through deployment, adoption, and value realization.
Develop and manage project plans, timelines, risks, and dependencies, providing clear status reporting to stakeholders.
Apply structured change-management practices to drive user adoption, training, and sustained utilization of digital solutions.
Identify high-value opportunities for process optimization, advanced analytics, and automation.
Develop proofs-of-concept leveraging data science, AI, or machine-learning techniques to address emerging business needs.
Lead and cultivate a user community focused on digital enablement, analytics literacy, and continuous improvement.