Carlos Castro Rey
About Carlos Castro Rey
Carlos Castro Rey is a Data Scientist with experience in developing solutions for predictive maintenance and natural language processing. He has worked at various institutions, including Universidad Pontificia Comillas and Minsait, and is currently employed at Keepler Data Tech.
Work at Keepler
Carlos Castro Rey has been employed at Keepler Data Tech as a Data Scientist since 2020. His role involves implementing advanced data solutions and leveraging cloud technologies. Notable projects include the development of a content generator use case utilizing Generative AI on Google Cloud Platform. Additionally, he has built a pipeline for natural language processing tasks using Microsoft Azure and has worked on a question/answering project that leverages Generative AI on AWS.
Previous Experience at Minsait
Before joining Keepler, Carlos Castro Rey worked as a Data Scientist at Minsait from 2019 to 2020. During his tenure, he contributed to various data-driven projects, enhancing his expertise in data science applications and methodologies. His experience at Minsait provided him with a solid foundation in data analytics and cloud computing.
Academic Background
Carlos Castro Rey studied at Universidad de Sevilla, where he pursued a degree in Mathematics. He completed his studies from 2015 to 2018, earning a Grado. This academic background has equipped him with strong analytical skills and a deep understanding of mathematical concepts, which are essential in his data science career.
Teaching Experience
From 2021 to 2023, Carlos served as an Associate Teacher at Universidad Pontificia Comillas ICAI-ICADE in Madrid. In this role, he contributed to the academic development of students by sharing his knowledge and experience in data science. His teaching experience complements his practical work in the field, allowing him to bridge theoretical concepts with real-world applications.
Technical Projects and Solutions
Throughout his career, Carlos Castro Rey has developed various technical solutions in data science. He created an anomaly detection solution for predictive maintenance using AWS cloud services, showcasing his ability to apply data science techniques to real-world problems. Additionally, he developed a platform for training and monitoring forecast models on Microsoft Azure, demonstrating his proficiency in cloud-based data solutions.