David Jaggi is an associate researcher and lecturer based in Zurich with eight years of experience at the intersection of finance, data science, and machine learning. He combines academic rigor as a PhD candidate in Alternative Data and Machine Learning with practical industry experience from roles at Credit Suisse and as an AI Research Engineer, applying quantitative methods to asset and wealth management. His background spans investment analysis, student-managed fund success, and hands-on technical training as an automotive mechatronics apprentice—an uncommon mix that grounds his data-driven research in real-world systems thinking. Active in both research and applied engineering, he translates alternative data insights into actionable models for investment contexts while teaching the next generation of practitioners.
8 years of coding experience
2 years of employment as a software developer
Stay Abroad English, Stay Abroad English at EC English Language Centres Alumni
MSc. Investment and Finance, MSc. Investment and Finance at Queen Mary University of London
The University of Melbourne
Vocational Baccalaureate Commerce, Vocational Baccalaureate Commerce at Vocational School Zurich
Engineering and Management Business Mathematics (International Profile), Engineering and Management Business Mathematics (International Profile) at ZHAW School of Engineering
Federal Diploma of Vocational Education and Training Automotive Mechatronic Technician, Federal Diploma of Vocational Education and Training Automotive Mechatronic Technician at Berufsbildungsschule Winterthur
PhD Candidate Alternative Data and Machine Learning, PhD Candidate Alternative Data and Machine Learning at University of Zurich
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