About

Qike (Max) Li is a highly skilled Staff Data Scientist with expertise in machine learning, deep learning, and probabilistic programming. He holds a Ph.D. in Statistics and has over 5 years of relevant experience in big data analytics, online experimentation, and A/B testing. Max has a solid background in Python, Spark, Tensorflow, Edward, PyTorch, Pyro, Docker, and K8s. He is also well-versed in NLP, Bayesian statistics, Gaussian process, anomaly detection, and explainable AI (XAI). Max is known for his effective communication and visualization of data, as well as his ability to transform ambiguous business needs into solid technical roadmaps and actionable tasks. Max has thirteen scientific papers to his name, six of which he authored as the first author. He also holds one US patent and has written six blog posts. Currently, Max works as a Staff Data Scientist at Wish, a decision optimization platform. In his role, he uses his expertise in machine learning and experimentation to drive business growth. Prior to joining Wish, Max worked as a Data Scientist at Doordash, where he led the generation of leads with query understanding and Gaussian process. He also estimated demand-supply gaps with probabilistic machine learning models, forecasted demand for promotions with Bayesian models, optimized UI with multi-armed bandits, and built personalized upsell recommendation systems with deep learning. Before Doordash, Max was a Data Scientist at Quantiply Corporation, where he leveraged machine learning/artificial intelligence to tackle financial fraud. He developed deep learning, nonparametric Bayesian, and probabilistic models to detect anomalous financial behaviors, built ensemble learning models with Bayesian and frequentist frameworks, and built explainable artificial intelligence (XAI) models. He also built automatic machine learning (AutoML) tools to democratize access to machine learning and led customer frustration modeling. Max received his Ph.D. in Statistics from the University of Arizona in 2017. He has a strong technical background in data science, research science, ML, deep learning, NLP, AI, Spark, big data, recommendation systems, and testing.