Internship Description
Jing Qian, ‘19SEAS, and Ethan T. Schmidt, ’19BUS interned at the Earned Asset Resource Network, Inc which works at the intersection of financial technology and economic inclusion to empower low-income Americans to take charge of their financial lives. As part of their partnership with the Columbia Business School Fintech Initiative, EARN was trying to help consumers build financial resilience as well as devise nudges to increase personal saving rates through the use of data analytics. Jing worked with the researchers to generate new insights through various forms of data analytics techniques, including Machine Learning and Statistical/Data Analysis. Ethan worked on developing data models that analyzed and predicted consumer spending and saving habits and behavioral triggers based on traditional financial histories. These students were supported with new SESF funding earmarked for financial inclusion.
In the 2019 summer as a SESF fellow, Jing Qian worked with Earn, a national non-profit helping working families achieve prosperity through savings and Columbia Business School Fintech Initiative. In this project, we studied the transaction behavior of low-to-moderate Americans, found patterns and will finally devise nudges to increase personal saving rates.
Jing's role in the project is a data analyst. Based on the collected transaction data. Jing performed Statistical and Machine Learning methods to analyze the pattern of people's behavior. These skills are exactly what Jing has learned from her current major: Machine Learning track in Computer Science. Moreover, Jing took a data analytics course from Department of Statistics. Other than data analyze, Jing also wrote documentations of the dataset which help future users read the data and perform further studies.
Although Jing is quite comfortable to do the data analysis, understanding why these finance-related data behave the way they are is a big challenge to her during the summer project. This big challenge is caused by multiple reasons. First, Jing is an international student and hence lack some common sense of the transaction data. Moreover, with her Physics and Computer Science background, finance is quite new to her. Luckily, her supervisor Professor Farrokhnia and other team members are nice and professional. They helped her a lot in understanding the numbers and forming hypothesis. She also took some time in introductory finance courses to understand the terms.
Jing's professional goal is to be a data scientist in the finance industry. The experience of working with professional team members is quite valuable to her. She cooperated with people from diverse background and learned how a project pipeline works through people with different functions. Also, analyzing real transaction data is different from course projects, she felt knowing people better and being able to do some help. Being trained in schools with the skills of engineers and scientists, she understands that aim of all the science and technologies is not only to explore the rules of nature, but also serve humanities, for the greater good of the society. The data matter, but what matters more is what we could learn from the data and help people improve their well being.