Nicholas De Goede, Cfa
About Nicholas De Goede, Cfa
Nicholas De Goede is the Director of Engineering, Machine Learning Client Solutions at Zest AI, specializing in deploying machine learning models for credit scoring in the Greater Los Angeles Area.
Title
Nicholas De Goede currently holds the position of Director of Engineering, Machine Learning Client Solutions at Zest AI, where he oversees the deployment of machine learning models to score millions of credit applications monthly. His work aims to increase credit access and reduce defaults.
Company
Nicholas De Goede is a key part of Zest AI in the Greater Los Angeles Area. At Zest AI, he delivers technical solutions and software tailored for highly regulated lending institutions. His role requires addressing the needs of internal and external regulatory bodies.
Previous Employment at Amazon
Nicholas De Goede worked as a Pathways Operations Manager at Amazon in Tracy, California, from 2017 to 2018. His tenure spanned one year, focusing on operational management.
Previous Employment at Google
In 2016, Nicholas De Goede had a stint as an MBA Summer Intern in Consumer Operations Analytics at Google. He worked for three months in the San Francisco Bay Area, gaining valuable industry experience.
Education and Expertise
Nicholas De Goede attended UCLA Anderson School of Management, earning a Master of Business Administration (MBA) with a focus on Technology from 2015 to 2017. Prior to this, he studied at UCLA, where he achieved a BS in Mathematics/Economics with a specialization in Computing and minors in Accounting and Statistics, from 2003 to 2007.
Background
Nicholas De Goede’s early career was marked by various roles at Dimensional Fund Advisors in Santa Monica, California. He started as a Research Assistant from 2007 to 2011, moved on to become a Senior Associate in Investment Analytics and Data from 2012 to 2014, and later served as a Senior Associate in Research from 2014 to 2015.
Expertise in Machine Learning and Data Engineering
Nicholas oversees machine learning model deployment to enhance credit applications and reduce defaults. His areas of expertise include data engineering, model development workflow, machine learning algorithms, and exceptional customer service.