A global pharmaceutical company (about 33,000 employees, offices in 18 countries) improved clinical trial design by building a machine-learning design hub to speed up trial country allocation, site selection, and management. The core challenge was long decision timelines and high operational costs in complex trial design, driving a need for a data-driven platform to optimize decision-making and resource allocation. The solution migrated and integrated internal and external data into a scalable AWS cloud environment, developed custom ML models (Python, AWS Lambda, PostgreSQL) for predictive trial parameters, and delivered a user-friendly analytics interface with training for adoption. Results included reducing country allocation scenario creation from weeks to minutes, projected $3M–$5M sav

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