Associate Director, Digital & Business Process Strategy

The Associate Director, Digital & Business Process Strategy leads the vision, roadmap, and value delivery of digital products that span PAS ‑ X , Tulip (MES), data historians, and LIMS within AstraZeneca’s manufacturing network. This role sits in the Digital & Business Process Strategy team and partners with Operations, Quality, Global IT, Data & AI, Supply Chain and Site teams to modernize data flows, improve Right ‑ First ‑ Time , and enable compliant, data ‑ driven decision making from shop floor to enterprise. You will be based in Rockville, MD and report to the Senior Director of Digital & Business Process Strategy. Key responsibilities 1. Product vision and roadmap Define and socialize a compelling product vision linking MES (PAS ‑ X , Tulip ), Historian - CDZ , and LIMS capabilities to business outcomes (yield, cycle time, batch release, deviation reduction) Build and manage a multi ‑ release roadmap, balancing global platforms with site needs, technical debt, and regulatory priorities. Maintain a prioritized, value ‑ ranked backlog with clear acceptance criteria and Definition of Done . 2. Stakeholder and value management Act as the voice of Operations, Quality, and QA/QP in product decisions; translate pain points into epics/features to deliver e2e business process maps and well-defined business requirements Quantify value through OKRs/KPIs (e.g., batch review time, exception rates, data availability SLAs) and track benefits realization. 3. Delivery leadership Lead agile squads (process SMEs, solution architects, data engineers, MES configuration, historians, LIMS specialists, validation) to deliver increments. Coordinate integration patterns between MES , historians, LIMS, ERP, data platforms, and analytics/AI products. Ensure robust data models, master data stewardship, and event/transaction standards supporting traceability across systems. 4. Compliance and validation Ensure products comply with GxP , 21 CFR Part 11/Annex 11, data integrity (ALCOA&#43;), and site/enterprise validation requirements. Own validation strategy for changes (CSV/CSA), including risk assessments, test evidence, and audit readiness. 5. Data and analytics enablement Enable contextualized data capture and lineage from equipment and MES to historian and LIMS; expose governed data to analytics platforms. Partner with Data & AI teams on use cases such as real ‑ time deviation detection, golden batch, CPV/Continued Process Verification, and eBR / eBRR optimization. 6. Operations readiness and lifecycle management Plan releases, cutovers, and change management across multiple sites; coordinate training and SOP updates. Own product performance, reliability, and cybersecurity posture; manage incidents, problem remediation, and continuous improvement. Manage vendor relationships (e.g., Körber , Factory Talk, PAS ‑ X, Tulip, H istorian and LIMS providers) and ensure contract deliverables align to roadmap. Required qualifications Bachelor’s degree in engineering, life sciences, computer science, or related field; advanced degree preferred. 8&#43; years in pharma/biotech or highly regulated manufacturing with hands ‑ on exposure to at least two of the following: PAS ‑ X (or equivalent MES), process data historians (e.g., PI/AVEVA, IP.21, Ignition), LIMS/ELN. 3&#43; years as a Product Owner or equivalent (product management, solution owner) delivering digital capabilities in GxP environments. Strong understanding of manufacturing and quality processes: recipe management, eBR , batch disposition, deviation/CAPA, CPV, stability, and sample lifecycle. Demonstrated experience integrating shop ‑ floor systems with enterprise platforms (ERP, QMS) and data/analytics stacks; familiarity with ISA ‑ 95/ISA ‑ 88 models and data standards. Practical knowledge of CSV/CSA, GAMP 5, data integrity, and audit expectations. Preferred qualifications Experience with PAS ‑ X configuration and content management; knowledge of interface frameworks, EBR review workflows, and exception handling. Background in OT/IT convergence: OPC UA, MQTT, edge connectivity, time ‑ series data modeling, and event streaming. Working knowledge of modern data platforms and analytics (e.g., cloud data lakes, <