I-Kang Ding

I-Kang is a data scientist and machine learning engineer who has over a decade of experience in a variety of fields, including financial services, semiconductor manufacturing, and solar cell R&D. He has years of hands-on experience in the full lifecycle of machine learning models at scale, from business problem definition and refinement, data and feature pipelines, model development, and model deployment and monitoring in production.

Prior to KoBold Metals, he was a data science manager at Capital One, where his work focused on developing machine learning models to detect and prevent various types of credit card fraud for the company’s entire credit card portfolios (with purchase volume equal to ~2% of US GDP), and deploying models to customer-facing production systems on AWS. He has also created inner-sourced Python packages and learning modules including self-directed trainings and in-person courses to empower hundreds of analysts to more easily automate their work with Python.

I-Kang completed his Ph.D. in Materials Science and Engineering from Stanford University. His Ph.D. thesis focused on the development of advanced characterization techniques to better understand the structure-property relationship of perovskite-family of solar cells. He is the author or coauthor of more than 15 peer-reviewed papers and holds 3 patents in design of advanced solar cells. He also holds a B.S. degree in Chemistry from National Taiwan University.