Victor Huang is a data scientist with 8 years of experience building production ML systems and experimental frameworks across marketing, fraud, and product teams at companies like Yelp, Wealthsimple, Canadian Tire, and Ritual. He blends strong mathematical foundations (UCLA Anderson, Applied Math, and CS graduate work) with hands-on engineering—developing ETL and Airflow pipelines, Flask APIs, PySpark monitoring, and deploying TensorFlow/PyTorch models. Notable wins include a one-stop campaign analysis engine that slashed analysis time from hours to minutes, a graph-embedding solution that boosted product-assortment accuracy by 15%, and RL-based customer lifetime value tooling. Comfortable with both frequentist and Bayesian experimentation and multi-armed bandits, he mentors junior peers and iterates quickly from prototypes in R/Julia to scalable Python production. An inquisitive learner (GitHub bio: "today I learn"), he pairs academic optimization work with practical product impact.
8 years of coding experience
4 years of employment as a software developer
Master of Science - MS, Data Analytics, Master of Science - MS, Data Analytics at UCLA Anderson School of Management
Bachelor's degree, Chemistry, Bachelor's degree, Chemistry at University of Pittsburgh
N.A., Theoretical Chemistry, N.A., Theoretical Chemistry at University of California, Los Angeles
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at Georgia Institute of Technology
Master of Arts - MA, Applied Mathematics, Master of Arts - MA, Applied Mathematics at York University
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