Jason Zhu
Associate Director – Real-World Data Engineer AstraZeneca
Jason Zhu is Associate Director of Real World Data Engineering at AstraZeneca, based in Toronto. He leads the design and development of data pipelines that transform heterogeneous real-world datasets into OMOP-standardized formats for evidence generation and research.
His work sits at the intersection of data engineering and applied AI — developing tools and workflows that accelerate traditionally manual processes in RWD, from data standardization and quality assurance to automated OMOP mapping. He is also a contributor to driving practical AI literacy across cross-functional teams within AZ.
Seminars
- Why do AI‑driven RWE initiatives so often succeed technically but fail organizationally, and where do most companies get stuck?
- How can RWE/HEOR leaders better align data scientists, analysts, and medical experts around shared goals, methodologies, and accountability?
- What practical approaches are teams using to validate AI outputs without slowing delivery or undermining confidence in the evidence?
- How should organizations address cultural resistance and skills anxiety when AI drastically changes timelines, roles, and workflows?
- Who ultimately owns AI‑generated evidence and what governance models support innovation while protecting scientific and regulatory credibility?
This interactive workshop moves beyond theoretical AI discussions to focus on practical, regulated, decision‑grade adoption of AI (including agentic AI) in real‑world evidence environments. The session will emphasize governance tradeoffs, enterprise implementation realities, and lessons learned from what has genuinely scaled, and what has failed.
This workshop will gather experts to discuss:
- What Has Fundamentally Changed with AI, Agentic AI, and Digital Innovation in RWE
- Trust, Transparency, and Trustworthiness Across the Data Lifecycle
- From Generic AI to Pharma‑Specific, Decision‑Grade Use Cases
- Governance, Validation, and Risk Mitigation in Regulated Environments
- Enterprise Implementation Lessons on Where AI Has Delivered & Where It Has Failed
- Building organizational readiness for AI‑enabled RWE
