Y S.
About Y S.
Y S. is a Data Scientist 2 currently working at Cvent in Vancouver, British Columbia, Canada. With a strong academic background in statistics and experience in various data science roles, Y S. specializes in Bayesian Data Analysis and Dimensionality Reduction techniques.
Work at Cvent
Currently, Y S. holds the position of Data Scientist 2 at Cvent, where they have been employed since 2021. Based in Vancouver, British Columbia, Canada, Y S. contributes to various data-driven projects within the organization. Their role involves applying advanced statistical techniques and data analysis methods to support decision-making processes and enhance operational efficiency.
Previous Experience in Data Science
Before joining Cvent, Y S. gained valuable experience in the field of data science through various roles. They worked as a Data Scientist at Suning USA from 2019 to 2020 in Palo Alto, California. Prior to that, Y S. served as a Data Scientist Intern at Airbnb for one month in 2017 in the San Francisco Bay Area. Additionally, they held a position as a Post Doctoral Fellow at Virginia Tech from 2018 to 2019.
Education and Expertise
Y S. has a strong academic background in statistics. They earned a Doctor of Philosophy (Ph.D.) in Statistics from Virginia Tech, where they studied from 2012 to 2018. Prior to that, they obtained a Master of Science (MS) in Statistics from Temple University from 2010 to 2012. Y S. also holds a Bachelor of Science (BS) in Statistics from the University of Science and Technology of China, completed from 2006 to 2010. Their expertise includes Bayesian Data Analysis, Dimensionality Reduction techniques, and proficiency in data processing tools such as Presto and Hive.
Technical Skills and Tools
Y S. employs various technical skills and tools in their data science work. They utilize Bayesian Data Analysis techniques for statistical modeling and apply Dimensionality Reduction methods to optimize machine learning models. Y S. is experienced in using Airflow for workflow automation and orchestration in data projects. Additionally, they leverage AWS for cloud-based data solutions, enhancing the scalability and efficiency of data processing.