Haochen Song
About Haochen Song
Haochen Song is a Data Scientist II at Expedia Group, specializing in Product Performances & Insights. He holds a Master's degree in Business Analytics from Imperial College London and has a background in Mathematics with Finance.
Current Position at Expedia Group
Haochen Song holds the position of Data Scientist II in the Product Performances & Insights team at Expedia Group. Haochen has been in this role since October 2020, focusing on analyzing and improving product performance through insights derived from data. His responsibilities include developing and deploying data models that enhance the overall customer journey and experience.
Previous Experience at Expedia Group
Prior to his current role, Haochen Song worked as a Data Analyst in the Marketing Science division at Expedia Group from 2018 to 2020. During this period, he was involved in various marketing analytics projects based in London, United Kingdom. His work included deriving insights from marketing data to support strategic decisions and campaigns.
Professional Role at KPMG
Haochen Song also gained significant experience working at KPMG in 2018. During his three-month tenure, Haochen was part of the Data Spark project as a Data Analyst. His responsibilities involved analyzing large datasets to provide business insights and support decision-making processes for clients.
Educational Background
Haochen Song has an extensive academic background, which includes a Master of Science (MSc) in Business Analytics from Imperial College London, attained between 2017 and 2018. He also earned a Bachelor of Science (BSc) degree in Mathematics with Finance from the University of Liverpool, where he studied from 2015 to 2017, in conjunction with Xi'an Jiaotong-Liverpool University where he studied from 2013 to 2017.
Research and Technical Contributions
Haochen Song has conducted notable research focused on feature importance models designed to enhance customer journey experiences. Additionally, he has developed clustering models aimed at identifying and addressing customer bad experiences, highlighting his contributions towards improving product performance and customer satisfaction through advanced data science techniques.