Christine Liang
About Christine Liang
Christine Liang is a Senior Business Analyst in Consumer Health at TELUS, based in Vancouver, British Columbia, Canada.
Current Role at TELUS
Christine Liang is currently serving as a Senior Business Analyst in the Consumer Health division at TELUS. Based in Vancouver, British Columbia, Canada, her role involves significant analytical responsibilities. As part of her contributions, she focuses on automating operational processes, optimizing query performance, and designing ETL pipelines.
Previous Experience at Dynatech Technology Corp
From 2017 to 2019, Christine Liang worked as a Business Analyst at Dynatech Technology Corp in Taichung City, Taiwan. During her two-year tenure, she gained valuable experience in analyzing business processes and contributing to operational improvements at the company.
Educational Background in Big Data and International Business
Christine Liang has an extensive educational background. She earned a Certificate in Big Data and Social Analytics from the Massachusetts Institute of Technology between 2017 and 2018. Additionally, she participated in an exchange program focused on International Business/Trade/Commerce at Keio University from 2015 to 2016. Earlier, she completed a Bachelor of Commerce degree, majoring in Marketing, from The University of British Columbia, where she studied from 2012 to 2016.
Diverse Internships and Early Career Roles
Christine Liang's early career is marked by various internship roles. In 2014, she worked as a Teaching Assistant at The University of British Columbia, a Rotational Analyst Intern at Coutts in Hong Kong, and a Corporate Finance Intern at MediaTek USA Inc. in Hsinchu County/City, Taiwan. These positions equipped her with a broad range of skills and insights, laying a solid foundation for her future roles.
Analytical and Predictive Modeling Skills
Christine Liang enjoys building analytical models to achieve marketing objectives. Her expertise includes developing clustering models for auto-segmentation, propensity models for customer lifetime value predictions, attribution models for channel evaluations, and collaborative filtering for cross-sell recommendations. She effectively uses predictive modeling for budgeting and operational planning, like creating models to identify stuck in queue drop tickets, enabling significant CAPEX savings for her organization.