I am a data scientist and machine learning engineer who has over a decade of experience in a variety of fields, including financial services, optoelectronic semiconductor manufacturing, and solar cell R&D. I have years 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.
Currently, I am at KoBold Metals, a climate tech startup working on leveraging data science and machine learning for mineral exploration of crucial lithium-ion battery materials.
Previously, I was a data science manager at Capital One for close to 5 years, where my work focuses on developing machine learning models to fight 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 the cloud. I have 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, and gave a PyCon talk with my colleagues about our lessons learned.
I completed my PhD in Materials Science and Engineering from Stanford University. My PhD thesis focused on the development of advanced characterization techniques to better understand the structure-property relationship of perovskite-family of solar cells. I also hold a B. Sc. degree in Chemistry from National Taiwan University.