AbbVie is dedicated to delivering a consistent stream of innovative, safe, and effective medicines and products that solve serious health issues and have a remarkable impact on people's lives. As a quality organization in research development, R&D Quality Assurance is committed to driving quality excellence across all stages of discovery and development for the product's lifecycle to achieve product realization and accelerate access to innovative therapies and products for our patients.

Patient needs, stakeholder demands, industry innovation, the regulatory landscape, and AbbVie's business strategy continue to evolve rapidly. Emphasizing and embedding quality in the execution of clinical trials, post-market vigilance, and post-market studies helps assure patient safety, maintain data integrity, foster regulatory compliance, and contribute to the overall success of drug/device development programs.

The Associate Director, R&D Quality Transformation and Data Science supports the development and execution of transformation initiatives across R&D Quality Assurance, integrating digital capabilities, analytics (including AI/ML), process improvement, and operational best practices. This role helps enable the use of data, insights, and emerging technologies to improve quality processes, strengthen quality culture, and support continuous improvement efforts across the organization.

This position is suited for a collaborative and analytical leader passionate about improving how R&D QA operates through data-driven decision-making, process optimization, and change management. The role partners with leaders across R&D Quality Assurance and cross-functional stakeholders to help translate organizational feedback, diagnostic findings, and business needs into practical improvement initiatives. This role reports directly to the Sr. Director, R&D Quality Transformation & Data Science.

Primary Responsibilities

1. Support the development and execution of a focused R&D QA transformation roadmap, including analytics, AI/ML, and operational excellence initiatives anchored to organization-wide priorities, regulatory requirements, and diagnostic insights.
2. Analyze survey, diagnostic, and performance data to identify trends, opportunities, and root causes, and help prioritize improvement actions targeting both quality culture and business operations.
3. Lead or support cross-functional transformation projects that improve quality operations, reporting, decision-making and ways of working.
4. Partner with and across R&D QA, IT, and R&D, to implement improvements and support adoption of new tools, methods, and operating models.
5. Contribute to the use of advanced analytics, automation, and AI/ML-enabled solutions to improve efficiency, consistency and insight generation.
6. Support development and refinement of functional KPIs and executive-level dashboards.
7. Support business cases through financial modeling, scenario analysis, quantitative benchmarking, and clear articulation of strategic and operational value.
8. Translate quality and operational data into clear business value narratives, including benefits, cost avoidance, and operational impact of selected improvement initiatives.
9. Monitor progress, risks, dependencies, and outcomes for assigned initiatives, and provide concise status updates and recommendations to leaders and stakeholders.
10. Promote collaboration, change management, continuous improvement, and capability building in digital tools, analytics, and process excellence across R&D QA.