Sherrine Eid
Global Head of Real-World Evidence & Epidemiology
Sherrine Eid, MPH is the Global Head of Epidemiology, Real-World Evidence, and Observational Research at SAS Institute, where she leads specialists in the RWE Working Group pioneering the integration of advanced analytics, machine learning, and responsible AI in life sciences research.
With over 25 years of experience in global health, public health, healthcare and pharma, Sherrine specializes in transforming real-world data into regulatory-ready evidence through innovative applications of ML/AI and GenAI. As SAS's Data Ethics Ambassador, she drives frameworks for AI governance, trustworthy AI, and responsible innovation across the healthcare ecosystem. During COVID-19, she led the Epi Response team whose predictive models directly informed CDC decision-making and clinical trials worldwide.
A prolific researcher and thought leader, Sherrine has published extensively in peer-reviewed venues and conference proceedings including work on propensity score matching algorithms, machine learning applications in personalized medicine for oncology, synthetic data strategies, and AI-augmented safety signal detection from patient narratives. Her peer-publications have been cited over 1,300 times and she has been a guest on the SAS Health Pulse Podcast and invited as a keynote or plenary speaker over 50 times.
Trained in transformational leadership and certified in the Demartini Method®, Values Facilitation, and executive coaching, Sherrine empowers professionals to align technical excellence with authentic purpose. She integrates evidence-based methodologies with strategic leadership to create transformative experiences.
Featured Presentations
Sherrine explores how epidemiology, real-world evidence, observational research, advanced analytics and responsible AI are transforming healthcare and life sciences. Drawing on decades of experience, she shares practical approaches to generating regulatory-ready evidence, applying machine learning and GenAI responsibly, strengthening AI governance, and accelerating better decisions for researchers, regulators and healthcare organisations.