Daniel Ibáñez

Daniel Ibáñez has a diverse work experience in the field of machine learning and data science. Daniel started their career as a CTO and Software Development Engineer at relevante.me in 2015, where they focused on keeping the team focused on delivering an MVP and making scalable decisions at a low cost. Daniel also had roles as a Senior Backend Software Engineer at Pentasoft Group, and as a CTO Partner at ApeLucy.

In recent years, Daniel has worked as a Machine Learning Engineer and Deep Learning Engineer, focusing on natural language processing (NLP). Daniel has experience in tasks such as text simplification, creating APIs, implementing techniques for text simplification, and researching and implementing new ways to improve text simplification for different languages.

Daniel has also worked as a Data Scientist (Research) at the Instituto Complutense de Ciencias Musicales and as a Machine Learning Engineer (NLP) at the Translation Centre for the Bodies of the European Union.

Overall, Daniel Ibáñez has a strong background in machine learning, NLP, and data science, with experience in various roles and industries.

Daniel Ibáñez obtained a Computer Engineer Degree in Information Science/Studies from Universidad Carlos III de Madrid, which they completed from 2002 to 2007. Prior to that, they earned a High Grade Teacher degree in French Horn interpretation from Real Conservatorio Superior de Música de Madrid, where they studied from 1994 to 2001. Additionally, they hold multiple certifications such as "Generative AI with Large Language Models" from DeepLearning.AI, "dbt Fundamentals" from dbt Labs, "Fundamentals of Machine Learning for Healthcare" from Stanford Online, and others.