Jason Tun

Jason Tun has a diverse work experience in data science and analytics. Jason is currently working as a Principal Data Scientist at Los Angeles Times, specializing in RecSys, NLP/LLM. Prior to this role, they served as a Senior Data Scientist with the same company, focusing on NLP/LLM.

Before joining Los Angeles Times, Jason worked at Fandango as a Data Scientist. In this position, they developed a personalized purchase timing model using machine learning models such as Logistic Regression, RNN (GRU, LSTM), Random Forest, and Gradient Boosted Tree (GBT). Jason processed a large amount of data and predicted the purchase probabilities of individuals.

Jason also gained experience as a Statistical Analyst Intern at Tyler Internal Medicine. And at the University of California, Los Angeles, they worked as a Research Assistant and Teaching Assistant. In this role, they worked on various projects including anomaly detection, physical actions prediction using EEG signals, sentiment analysis of IMDB movie reviews, and animal adoption prediction.

Overall, Jason Tun has demonstrated expertise in data science, machine learning, and analytics through their various roles in different industries.

Jason Tun has a Master of Science degree in Statistics from UCLA, which they obtained in 2018. Prior to that, they completed their Bachelor of Science degree in Applied Math with a minor in Statistics at UCLA in 2016. Jason has also obtained several certifications in various fields, including Algorithmic Toolbox, Machine Learning, Natural Language Processing, SQL for Data Science, and Tableau 10 for Data Scientists. These certifications were obtained from Coursera, LinkedIn, and the Society of Actuaries.