Data Science Lead, Innovation Manager
Unicef
Daniel is a data scientist driven by a passion for using data to solve complex, real-world challenges for social good. With expertise in predictive modeling, financial risk management, and humanitarian aid delivery, his work spans economic litigation, policy implementation, and expense anomaly detection.
He holds degrees from Brown, Columbia (SIPA), and UC Berkeley, blending economics, public administration, and data science. Skilled in Python, R, SQL, and AWS, Daniel applies advanced analytics to uncover insights across diverse domains. His mission is to bridge data and policy to design effective, evidence-based solutions that drive impact in both public and private sectors.