Jeffrey Yau
Head of Data Science, Store Associate Technology Walmart Labs
Jeffrey is a VP of Data Science, Data Engineering, and Platform Engineering at the Store Associate Technology of Walmart Global Technology. Some of his prior roles include the Chief Data Scientist at AllianceBernstein, a global asset-management firm that managed over $550 billions, Vice President and Head of Data Science at Silicon Valley Data Science, and Vice President and Head of Risk Analytics and Quantitative Research at Charles Schwab Corporation. He has taught econometrics, statistics, and machine learning at UC Berkeley, Cornell, NYU, University of Pennsylvania, and Virginia Tech. Jeffrey is active in the data science community and often speaks at data science conferences and local events. He has many years of experience in applying a wide range of econometric and machine learning techniques to create analytic solutions for financial institutions, businesses, and policy institutions. Jeffrey holds a Ph.D. and an M.A. in Economics from the University of Pennsylvania and a B.S. in Mathematics and Economics from UCLA.
WATCH LIVE: November 17 @ 1:50PM – 2:10PM ET
Forecasting is both a fascinating subject to study and an important technique applied in industry, government, and academic settings. Examples in retail applications include demand and inventory planning, marketing strategy planning, capital budgeting, pricing, machine predictive maintenance, supply chain forecasting, industry trend forecasting, and macroeconomic forecasting. In this 15-minute presentation, I will summarize and compare a few time-tested, classical statistical time series techniques that have been very popular among practitioners for decades and some new machine learning based techniques that have gained their popularity recently.