Gabriel Espadas Pech
About Gabriel Espadas Pech
Gabriel Espadas Pech is a Senior Data Scientist at Ocado Technology and a Data Scientist at Dilax Intelcom, with extensive experience in machine learning and data analysis. He has a strong academic background in statistics and has held various roles in financial and research institutions across Europe.
Work at Ocado Technology
Gabriel Espadas Pech has been employed at Ocado Technology as a Senior Data Scientist since 2018. In this role, he focuses on developing, improving, and prototyping innovative machine learning services. His work contributes to enhancing the efficiency and effectiveness of data-driven solutions within the organization, which is known for its advanced technology in online grocery retail.
Work at Dilax Intelcom
Since 2017, Gabriel has worked as a Data Scientist at Dilax Intelcom in Berlin, Germany. He is involved in the Public Mobility division, where he develops and prototypes machine learning services. His role emphasizes the application of data science techniques to improve public transportation systems and mobility solutions.
Education and Expertise
Gabriel Espadas Pech holds a Master of Science in Statistics from ETH Zürich, which he completed in 2016. Prior to this, he earned a Bachelor of Science from Universidad Autonoma De Yucatan in 2009. His educational background provides a strong foundation in statistical analysis and data interpretation, which supports his work in data science and machine learning.
Background in Financial Services
Gabriel has significant experience in the financial sector, having worked at Swiss Re in various roles. He served as Assistant Vice President Team Leader in Business Management from 2012 to 2014 and as Associate Business Analyst from 2010 to 2012. His managerial experience and knowledge of financial and probabilistic models stem from these positions, enhancing his analytical capabilities.
Research Experience
In 2016, Gabriel worked as a Scientific Intern at Eawag, focusing on Applied Bayesian Data Analysis. During this four-month internship, he developed a novel method to calibrate hydrological models using machine learning techniques. This experience contributed to his expertise in applying advanced data analysis methods to real-world problems.