Stefanie Molin is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security. Her work revolves around data wrangling and visualization, building tools for gathering data, and knowledge sharing, the unglamorous but critical work of turning raw information into something analysts and engineers can actually use. She is also an active open source contributor and runs hands-on workshops that help people level up their pandas and data analysis skills.

Stefanie is the author of Hands-On Data Analysis with Pandas, a well-regarded book now in its second edition, which guides readers through the pandas library and the wider Python data science stack using real datasets, from earthquakes and weather to stock prices, covering everything from data collection via APIs to visualization and machine learning foundations.

Her academic background anchors this practice. She holds a bachelor of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science and is pursuing a master’s degree in computer science, with a specialization in machine learning, at Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages, spoken among both people and computers.