Jing Zan is an accomplished professional in data science and applied mathematics with extensive experience across various industries. At Zilliant, Jing led the development of methodologies and models for B2B sales opportunity assessments, focusing on statistical modeling for cross-selling and customer attrition, while also enhancing pricing strategies for Fortune 500 clients. Following this role, Jing served as Lead Risk Data Scientist at PayPal, developing solutions for merchant fraud detection and credit management. Subsequent positions included Senior Applied Scientist at Uber and Senior Data Scientist at Roblox, followed by Staff Data Scientist at Asana. Currently, Jing works as a Software Engineer specializing in Machine Learning at Meta. Jing holds a Ph.D. in Operations Research and Industrial Engineering from The University of Texas at Austin and has a background that includes a research internship at BNSF Railway, where optimization and programming skills were applied.
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