Carlos Xu
About Carlos Xu
Carlos Xu is a Data Scientist with a background in statistics and machine learning. He has held various positions in academia and finance, including roles at Harvard University and Element Capital Management.
Work at Hidden Road
Carlos Xu has been employed at Hidden Road as a Data Scientist since 2023. In this role, he applies his expertise in data analysis and statistical modeling to support the company's objectives. His responsibilities include analyzing complex datasets, developing predictive models, and contributing to data-driven decision-making processes.
Previous Experience in Data Science and Finance
Prior to his current position, Carlos Xu worked as a Quantitative Researcher at Squarepoint Capital from 2020 to 2022 in London, England. He also held roles at Element Capital Management, where he served as a Desk Strategist from 2017 to 2018 and as a Summer Analyst in 2016. His experience spans various aspects of finance and data analysis, enhancing his skills in quantitative research.
Teaching Experience at Harvard University
Carlos Xu served as a Teaching Fellow at Harvard University for Stat 110 in 2015 and Stat 123 in 2017. During these three-month tenures, he assisted in teaching statistical concepts and methodologies to undergraduate students, contributing to their understanding of the subject matter.
Educational Background
Carlos Xu holds a Bachelor of Arts in Statistics and Computer Science from Harvard University. He furthered his education by obtaining a Master of Science in Machine Learning from University College London (UCL). His academic background provides a strong foundation for his work in data science and quantitative analysis.
Early Career and Internships
Carlos Xu began his career with internships at BlackRock and Element Capital Management. He worked as a Summer Analyst at BlackRock in 2015 for two months and at Element Capital Management in 2016 for three months. These early experiences helped him gain practical insights into the finance industry and data analysis.