Daniel de Roux is a PhD candidate at Carnegie Mellon University from August 2019 to July 2024, specializing in Mathematics and Computer Science. Previous experience includes roles as a researcher at Quantil, where a text classification algorithm for financial fraud detection was developed, and at Alianza CAOBA, where a methodology for detecting fraudulent tax fillings was created. Additionally, Daniel served as a data scientist at Barbara & Frick, leading a team to develop machine learning models for customer attrition detection, and has also worked at Google in a data scientist capacity. Educational qualifications include a Bachelor's degree in Mathematics from the University of the Andes, two Master's degrees in Mathematics and Computer Science from the same institution and Carnegie Mellon University, respectively.
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