Summary
Pablo Guarda is a scientist and data-driven product leader with 8 years of experience applying machine learning, econometrics and optimization to transportation and marketplace problems. He currently leads experimentation and measurement for guest vertical products at Uber for Business, translating three-sided marketplace signals into product decisions. His PhD from Carnegie Mellon fused neural networks and computational graphs with classical travel-behavior models to estimate network-wide demand and enable fast traffic simulation — work that spawned open-source code, academic papers, a patent and a production Social Digital Twin at Fujitsu. Comfortable shipping end-to-end systems, he has built computer vision pipelines using satellite imagery, deployed ML traffic estimators at city scale, and prototyped GIS tools for network planning. Colleagues describe him as interdisciplinary and impact-focused: he combines cognitive science intuition with rigorous engineering to tackle complex, societally meaningful analytical challenges.
8 years of coding experience
8 years of employment as a software developer
Master of Science - MS, Transportation Engineering, Maximum Distinction (summa cum laude), Master of Science - MS, Transportation Engineering, Maximum Distinction (summa cum laude) at Pontificia Universidad Católica de Chile
Master of Science - MS, Machine Learning, Master of Science - MS, Machine Learning at Carnegie Mellon University School of Computer Science
Doctor of Philosophy - PhD, Civil and Environmental Engineering, Doctor of Philosophy - PhD, Civil and Environmental Engineering at Carnegie Mellon University
University College London
English, Spanish