Pablo Romano PhD
About Pablo Romano PhD
Pablo Romano, PhD, serves as the Lead Data Scientist at The New York Times since 2021, bringing over six years of experience in machine learning and natural language processing. His previous roles include positions at USAA, St. Edward's University, and the University of Oregon.
Work at The New York Times
Pablo Romano has served as the Lead Data Scientist at The New York Times since 2021. In this role, he focuses on leveraging data science techniques to enhance the organization's capabilities in data analysis and machine learning. His work contributes to the development of data-driven strategies that support editorial and business objectives.
Previous Experience in Data Science
Prior to his current position, Pablo Romano held several roles in data science. He worked as a Lead Data Scientist at USAA for a brief period in 2021 and previously served as a Senior Data Scientist at the same organization from 2018 to 2021. Additionally, he completed a Data Science Fellowship at Insight Data Science in 2018, which provided him with practical experience in the field.
Academic Background
Pablo Romano earned his Bachelor’s Degree in Chemistry from St. Edward's University, where he studied from 2008 to 2012. He furthered his education at the University of Oregon, obtaining a Doctor of Philosophy (Ph.D.) in Physical Chemistry from 2012 to 2017. His academic background has laid the foundation for his expertise in data science and machine learning.
Research Experience
Romano has extensive research experience, having worked as a Graduate Research Fellow at the University of Oregon from 2012 to 2017. He also served as a Research Assistant in the Department of Chemistry at St. Edward's University from 2009 to 2012. His research has focused on machine learning model development and implementation, particularly in natural language processing.
Expertise in Machine Learning
Pablo Romano has over six years of experience in both academic and industry roles, specializing in machine learning model development. His work includes the development of machine learning and artificial intelligence systems aimed at addressing natural language processing challenges. This expertise is integral to his contributions in various data science positions.