Daniel has substantial experience analysing large datasets, visualizing results, and providing intelligible insights. For example, he recently coded and ran an incentivized online experiment testing different instructional and survey designs to improve comprehension and survey quality.
In terms of sector expertise, he gained extensive knowledge in predicting specific product sales globally, modelling different gas supply & demand scenarios and modeling economic & political country risk for internal financing purposes at BASF. He also gained some experience in automated business reporting for money market products and in asset management at Amundi Deutschland, where he also handled minor client requests and prepared sales presentations. He applied various models including difference-in-differences, fixed effects, and probit models. He is especially interested in the use of econometric methods, game theory, behavioral science, and machine learning algorithms in real world applications.
Daniel holds a Master's and Bachelor's Degree in Economics from Heidelberg University, where he focused on econometric methods, game theory, and behavioral science and taught a scientific writing course. He also studied a year at the Warsaw School of Economics in Poland.
A native German speaker, he is also fluent in English and has basic knowledge of French.