Brenda Dongbo Zhang, Cfa
About Brenda Dongbo Zhang, Cfa
Brenda Dongbo Zhang is a Senior Data Scientist with extensive experience in quantitative research and data science. She has worked at notable organizations including Refinitiv and Thomson Reuters, and currently contributes to Shift, where she develops various models for product recommendations and pricing strategies.
Current Role at Shift
Brenda Dongbo Zhang serves as a Senior Data Scientist at Shift, where she has been employed since 2021. In her role, she has contributed to various projects, including the creation of a customer affinity model for product recommendations and sorting on the Shift website. She has also worked on developing pricing model components and has led the development of a recommendation model for personalized marketing emails. Her expertise extends to implementing end-to-end pipelines for ETL processes, model training, and production model deployment using AWS services.
Previous Experience at Refinitiv
Prior to her current position, Brenda worked at Refinitiv as a Senior Quantitative Researcher/Data Scientist from 2018 to 2021. During her tenure, she focused on quantitative research and data science methodologies, contributing to the development of analytical solutions. Her experience at Refinitiv provided her with a solid foundation in data analysis and modeling, which she has applied in her subsequent roles.
Educational Background
Brenda holds multiple degrees in finance, accounting, and computer science. She completed her Bachelor's degree in Finance and Accounting at Zhejiang University, where she transferred from Mathematics. She furthered her education with a Master's degree in Quantitative and Computational Finance from Georgia Institute of Technology. Additionally, she earned a Master's degree in Computer Science from the same institution. Brenda also participated in an exchange program at Purdue University, focusing on Data Mining Research.
Contributions to Data Science Projects
At Shift, Brenda has developed several models and tools that enhance data-driven decision-making. She created a click-through model to recommend and rank similar vehicles for users with limited search results. Additionally, she designed a lead scoring model to prioritize leads for sales operations. Her work includes creating a marketing mix model and a simulation tool, which she implemented as a Directed Acyclic Graph (DAG) in Airflow. She also developed vehicle embeddings using deep learning models.
Experience at Thomson Reuters
Brenda's career includes a significant role at Thomson Reuters, where she worked as a Quantitative Researcher/Data Scientist from 2012 to 2018. In this position, she engaged in quantitative research and data analysis, contributing to various projects that required advanced analytical skills. Her experience at Thomson Reuters further solidified her expertise in the field of data science.