As a customer-facing fintech company, Earnin has access to various types of valuable customer data, from bank transactions to GPS location. Leveraging cutting-edge machine learning models with these datasets enables highly impactful, smarter business decisions. However, the business impact and user experience highly depend on what models are being built and how they are being used. Meanwhile, robustness and scalability are also important factors for applications.
Ji Peng shares how Earnin uses unique datasets to build machine learning models and navigates the challenges of prioritizing and applying machine learning in the fintech domain.
Ji Peng is a problem solver who has developed the data backbone of Earnin, a high-growth financial company that allows anyone with a job and a bank account to get paid the minute they leave work. He has built machine learning systems that have given payroll flexibility to employees from more than 50,000 employers, guiding his data science team as they create sophisticated models to better understand the Earnin community. Ji holds a PhD from the University of Colorado, Boulder, and a BS from the University of Science and Technology of China (USTC).
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