Summary
Alexander Buchholz is an AI/ML and quantitative researcher with 10 years of experience applying Bayesian statistics, causal inference and large-language models to real-world problems across industry and academia. He has driven personalization and counterfactual evaluation at Amazon Music, built LLM-driven code generation at AWS, and now leverages that blend of research and product focus at QuantCo while lecturing AI at ESMT Berlin. Holding a PhD in Applied Mathematics and advanced degrees in statistics and economics, he has held research posts in Cambridge, Boston, Paris and Berlin, bringing rigorous probabilistic thinking to production ML. His Github work centers on computational Bayesian statistics, reflecting a long-standing preference for principled uncertainty quantification rather than purely black-box models. Colleagues describe him as equally comfortable devising new statistical methodology and shipping scalable ML systems, with a knack for turning theoretical insight into reproducible tools. Based in Berlin, he combines cross-border academic experience with hands-on industry impact in personalization, generative models and decision-focused evaluation.
10 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Mathematics and Statistics, Doctor of Philosophy (Ph.D.) Mathematics and Statistics at ENSAE Paris
Visiting PhD student Statistics, Visiting PhD student Statistics at Harvard University
Master of Science - MS Mathematical Statistics and Probability, Master of Science - MS Mathematical Statistics and Probability at Humboldt-Universität zu Berlin