Summary
Daniel Montero is a PhD student in mathematics focused on machine learning, combining eight years of applied data science experience with rigorous mathematical training. Based in Erlangen, Germany, he transitioned from multi-year data scientist roles at GMV to doctoral research at FAU's MoD Research Center, where he blends theory and application in ML. He holds an MSc in Mathematics and Applications and completed Coursera’s Deep Learning specialization with top marks, reflecting both formal and hands-on deep learning expertise. Daniel’s background in digitalization and big data analysis and his international education across Spain and Germany give him a pragmatic, research-driven approach to building ML solutions. Notably, he brings the perspective of a mathematician who codes—prioritizing foundational understanding that informs robust modeling and analysis.
8 years of coding experience
4 years of employment as a software developer
MSc in Mathematics and Applications, Mathematics, MSc in Mathematics and Applications, Mathematics at Universidad Autónoma de Madrid
Deep Learning Specialization 5 Parts Course, Machine Learning, 99%, Deep Learning Specialization 5 Parts Course, Machine Learning, 99% at Coursera
Bachelor's degree, Mathematics, Bachelor's degree, Mathematics at Universidad Complutense de Madrid
Baccalaureate of Excellence, Science, Baccalaureate of Excellence, Science at San Mateo
Specialist in Digitalization and Big Data Analysis Course, 9.5, Specialist in Digitalization and Big Data Analysis Course, 9.5 at Afi Escuela de Finanzas
Spanish, English, German, French