Marco F. Larrea Schiavon
About Marco F. Larrea Schiavon
Marco F. Larrea Schiavon is a Senior Data Scientist currently working at Umba in Mexico City. He has a strong academic background in Mathematics, holding a PhD from the University of Leeds and has previously held positions at LASI and Synx.co.
Current Role at Umba
Marco F. Larrea Schiavon serves as a Senior Data Scientist at Umba, a position he has held since 2021. Based in Mexico City, he applies his extensive knowledge in data science to develop innovative solutions. His role involves leveraging advanced data analytics techniques to drive business decisions and enhance operational efficiency.
Previous Experience at LASI
Prior to his current role, Marco co-founded LASI, where he served as Chief Technology Officer from 2020 to 2021. During his tenure in Mexico City, he contributed to the company's technological direction and development, focusing on data-driven strategies and solutions.
Educational Background in Mathematics
Marco holds a Bachelor of Science in Mathematics from Universidad Nacional Autónoma de México, completed between 2007 and 2011. He furthered his education with a Master's degree in Mathematics at the same institution from 2012 to 2015. Marco then pursued a Doctor of Philosophy in Mathematics at the University of Leeds from 2015 to 2019, where he focused on Mathematical Logic and categorical models for Homotopy Type Theory.
Experience in Data Science
Marco has a robust background in data science, having worked as a Senior Data Scientist at Synx.co from 2019 to 2020. His work included implementing models for fraud prediction, credit scoring, and intelligent budget allocation using multi-armed bandits. He has developed skills in creating data processing pipelines, visualizing complex results, and manipulating databases.
Research and Technical Skills
During his academic career, Marco gained experience with computer proof assistants such as Coq and Agda. He interned at the Mexican National Genomics Institute (INMEGEN), where he analyzed genomic data using R and the Bioconductor library. His research interests include natural language processing, computational vision, statistical data analysis, and reinforcement learning.