Javier Guzmán Figueira Domínguez

Javier Guzmán Figueira Domínguez has a diverse background in machine learning engineering and software development.

Starting with their most recent position at StatsBomb as a Machine Learning Engineer, they contribute their expertise in machine learning to the team.

Prior to that, they worked at DefinedCrowd Corp., where they held multiple roles. As a Senior Machine Learning Engineer, they focused on overseeing the quality of speech data delivered to clients. Javier Guzmán developed various components including a micro-service, a binary classification model, and an on-demand metrics computation system. Javier Guzmán also worked closely with stakeholders to understand their needs and guide them in utilizing the solutions. As a Machine Learning Engineer, they contributed to ensuring the quality of data submitted by the crowd, productizing metrics and models, and improving CI/CD and packaging processes.

Before DefinedCrowd Corp., Javier worked at Gradiant, where they worked on finding the best techniques and models to solve emerging challenges. Javier Guzmán also provided support in the design and deployment of solutions, including recommendation systems, clustering techniques, pattern mining, HMMs, and RNNs. In addition, they were an R&D Engineer in the eLearning analytics department, where they researched and developed solutions to help educational publishers transition to the digital world.

Javier also has experience as a Software Architect & FullStack Developer at gradox, where they co-founded and developed a collaboration platform for students. Javier Guzmán utilized a microservice-based architecture, utilizing technologies such as Netflix OSS stack and Spring framework. Javier Guzmán also worked as a Full Stack Engineer at 2MARES and as an Android Developer at chattyhive.

Overall, Javier Guzmán Figueira Domínguez has a strong background in machine learning engineering, software development, and collaborative platform development.

Javier Guzmán Figueira Domínguez has a diverse educational background, with a strong focus on both artificial intelligence and music performance. Javier Guzmán'seducation history is as follows:

From 2019 to 2023, Javier pursued a Doctor of Philosophy (PhD) degree in Artificial Intelligence at the Universidade da Coruña. Javier Guzmán'sresearch and studies concentrated on the field of artificial intelligence.

Prior to that, from 2017 to 2019, they obtained a Master's degree in Artificial Intelligence Research, specializing in Machine Learning and Data Science, at the Universidad Internacional Menéndez Pelayo. During this program, they delved into the field of artificial intelligence with a specific focus on machine learning and data science.

Javier's academic journey began in 2012 and concluded in 2016 when they earned an Engineer's degree in Informatics Engineering from the Universidad de Santiago de Compostela. This program provided him with a solid foundation in computer engineering.

In addition to their educational accomplishments in the field of technology, Javier also has a musical background. From 2009 to 2011, they pursued a Master's degree in Music Performance, specializing in violin, at Erasmushogeschool Brussel. Javier Guzmán further refined their musical skills during this period.

Lastly, from 2005 to 2009, Javier obtained a Bachelor's degree in Music Performance, with a specialization in violin, from Conservatorio Superior de Vigo. This degree laid the groundwork for their musical career.

To complement their academic qualifications, Javier has also obtained several certifications. In September 2018, they completed the "Python for Data Science" certification with IBM. Additionally, in June 2017, they completed the "Machine Learning" certification with Coursera. Furthermore, in July 2017, they received the "MongoDB for Developers Course Completion Confirmation" from MongoDB.

Overall, Javier Guzmán Figueira Domínguez's education history reflects a well-rounded combination of technical expertise in artificial intelligence and practical proficiency in music performance.