Lucy Z.
About Lucy Z.
Lucy Z. is a Senior Data Scientist at Wealthsimple in Montreal, Quebec, Canada, specializing in data-driven marketing solutions and proficient in SQL, Python, R, and PySpark.
Current Position at Wealthsimple
Lucy Z. has been a Senior Data Scientist at Wealthsimple since 2022. Based in Montreal, Quebec, Canada, she contributes to the company's data-driven solutions in financial services. Her role involves employing sophisticated statistical methods, including causal inference and both frequentist and Bayesian approaches. Her daily toolkit includes technologies such as SQL, Python, R, and PySpark, enabling her to perform data pipelining/ETL and exploratory analysis efficiently.
Previous Role at Shopify
Before joining Wealthsimple, Lucy Z. worked at Shopify as a Senior Data Scientist from 2017 to 2022. During her five-year tenure in Montreal, Quebec, Canada, she focused on leveraging data to tackle complex marketing problems. She specialized in using regression analysis, often referred to as Machine Learning, for building predictive models that enhance ecommerce strategies.
Early Career Experience at Catalyst Canada
Lucy Z. began her professional journey at Catalyst Canada where she served as an Account Manager - SEM from 2015 to 2017. Based in Toronto, she managed search engine marketing accounts for various clients, laying the groundwork for her expertise in data analytics and marketing.
Educational Background
Lucy Z. holds a Master's degree in Management Analytics from the Smith School of Business at Queen's University, completed in 2019. She also earned a Bachelor of Commerce (BCom) from McGill University, where she studied International Business, Economics, and Political Science from 2009 to 2013. Additionally, she spent an exchange year at the University of Lausanne, furthering her education in commerce.
Technical Skills and Specializations
Lucy Z. is proficient in SQL, Python, R, and PySpark. Her technical acumen includes data pipelining/ETL, exploratory analysis, and regression analysis, often referred to as Machine Learning. She applies both frequentist and Bayesian statistical methods as well as causal inference in her work to derive actionable insights from data.