Kuang Ting Chang
About Kuang Ting Chang
Kuang Ting Chang is the Associate Director of Data Science at Penguin Random House in New York, with over five years of experience in the data science field, particularly in the publishing industry.
Current Role at Penguin Random House
Kuang Ting Chang currently holds the position of Associate Director Of Data Science at Penguin Random House. Based in New York, New York, this role involves overseeing various data-driven initiatives and projects. Chang has demonstrated a steady career progression within the company, transitioning from Data Scientist to Senior Data Scientist before achieving their current role.
Previous Positions at Penguin Random House
Starting in 2018, Kuang Ting Chang joined Penguin Random House as a Data Scientist and held this role for two years in the Greater New York City Area. In 2020, Chang was promoted to Senior Data Scientist, a position they held for one year. This experience significantly contributed to their expertise in the publishing industry's data science applications.
Career at Genesis Research
Before joining Penguin Random House, Kuang Ting Chang worked as a Data Analyst at Genesis Research in Hoboken, NJ, from 2016 to 2017. During this period, they contributed to data-driven research projects, further refining their analytical skills and gaining valuable experience in the field of data science.
Entrepreneurial Experience at KrakenPrint
From 2013 to 2014, Kuang Ting Chang co-founded KrakenPrint in Taipei City, Taiwan. In their role as Vendor Relations Manager, Chang demonstrated entrepreneurial abilities and managed relationships with various vendors. This experience added a unique dimension to their skill set, showcasing versatility and leadership.
Educational Background
Kuang Ting Chang earned a Master of Arts (M.A.) from New York University in 2015 and holds a Bachelor of Arts (B.A.) from Yuan-Ze University, completed in 2012. This strong academic background in both arts and data science underscores their multifaceted expertise and foundational knowledge.