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James Pai has four years of work experience in the field of data science. In 2020, they worked as a Sr Data Scientist at Machinify, Inc. From 2019 to 2020, they worked as a Data Scientist at Eversight, where they led development of ML features and statistical analysis on optimal pricing experiments platform, designed large-scale sales forecast models by ARIMA with self-hyperparameter-tuning and improved by 12%, and explored transaction logs and devised seasonal stock classifiers by P/ACF analysis with 96% accuracy. From 2016 to 2019, they were a Machine Learning Engineer at JLM Energy, Inc., where they led and deployed a AI functionality and ML pipeline on energy storage products to automate energy cost savings in dynamic pricing scenarios by forecasting energy supply and demand, implemented ML algorithms from scratch including neural network, K-means, HMM and SGD, and enhanced products’ cost-saving ability by 38% and achieved commercial customers’ monthly saving goals. In 2015, they worked as a Data Scientist Intern at EEme, where they developed residential energy disaggregation algorithms in Python by HMM and regressions with 72% accuracy and designed API parser and data preparation algorithms to enhance efficiency of main pipeline by 25%.

James Pai obtained their Bachelor of Science (BS) in Hydraulic and Ocean Engineering from National Cheng Kung University between 2009 and 2013. James then attended Columbia University's School of Continuing Education in 2010. In 2014, they earned a Master of Science (M.S.) in Advanced Infrastructure System from Carnegie Mellon University. In addition, they have obtained several certifications from Coursera and deeplearning.ai, including Deep Learning, Sequence Models, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Neural Networks and Deep Learning, Structuring Machine Learning Projects, and Convolutional Neural Networks.