Peter Drembelas

Peter Drembelas is a seasoned data scientist currently holding the position of Data Scientist II at MUFG since April 2018, specializing in modeling for Treasury, Credit, and Fraud, utilizing various regression and machine learning techniques including kNN, Random Forest, and SVM. Prior to this role, Peter worked as a Data Scientist I at 2nd Payer Finance, Inc. and as an Advisor in Financial Engineering, contributing to innovative software solutions aimed at enhancing credit ratings and managing financial risk. Additional experience includes asset management research at Wilshire Associates and quantitative analytics at First Quadrant, where Peter developed infrastructure for trading and research on financial instruments. Educational qualifications comprise a Bachelor of Science in Electrical Engineering from Columbia Engineering, a Master of Financial Engineering focusing on Data Science from UCLA Anderson, and advanced studies in Deep Learning from Stanford University, alongside a Master of Science in Electrical Engineering from USC Viterbi School of Engineering.