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Pedro Urrego Gómez

Machine Learning Engineer at Factored

Pedro Urrego Gómez has a diverse work experience in the field of engineering. Pedro recently worked at Factored as a Machine Learning Engineer starting in March 2023. Prior to that, Pedro was a Machine Learning Trainee at Factored from November 2022 to February 2023. Before joining Factored, they worked at Area Ingenieros Consultores as a Project Engineer from September 2018 to April 2023. Pedro also gained experience as a Praktikant at ZILCH + MÜLLER INGENIEURE GmbH from October 2017 to February 2018.

Pedro Urrego Gómez has a diverse education history that spans various fields and institutions. Pedro obtained a Master of Science (MS) degree in Applied Mathematics from Universidad EAFIT starting in 2022. Prior to that, in 2020, they completed a Virtual Design and Construction Graduate Certificate (VDC) program at Stanford University.

In 2017, Pedro attended RWTH Aachen University, where they studied Bauingenieur (Civil Engineering). Pedro then pursued a Corporate Finance Specialist program at Escuela de Ingeniería de Antioquia in 2019.

Pedro's undergraduate education was also at Escuela de Ingeniería de Antioquia, where they earned a Bachelor of Science (BS) degree in Civil Engineering from 2013 to 2018.

In addition to their formal degrees, Pedro has obtained several certifications in various topics. These include Data Engineering with AWS Nanodegree from Udacity in February 2023, Convolutional Neural Networks from DeepLearning.AI in January 2023, and Deep Learning with Pytorch from DataCamp in October 2022.

Pedro has also acquired several certifications in data science and software engineering from DataCamp. These include certifications in supervised machine learning, efficient code writing, data cleaning, data ingestion, web scraping, and Python programming fundamentals. These certifications were obtained between 2021 and 2022.

Throughout their education journey, Pedro Urrego Gómez has pursued a wide range of subjects, demonstrating their passion for interdisciplinary learning and professional development.

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