Hagop Dippel
Hagop Dippel is an Applied Scientist at Zalando, where he focuses on building demand forecasting and inventory optimisation applications. He particularly enjoys bringing research ideas to end-2-end production systems. He’s passionate about deep learning applied to real-world industry use cases and human centred AI. Hagop studied Data Science and Econometrics at the Aix-Marseille University in France. In his free time, you can find him cycling or running (just contact him if you want to jog and/or chat!)
Sessions
It’s common to hear about demand forecasting in the e-commerce ecosystem. Indeed, It plays a pivotal role in logistics and inventory applications. However, due to uncertainty impacting demand and the stochastic nature of most downstream applications, the need for probabilistic demand forecasting emerges. Moreover, for the most realistic use cases, you’ll have to forecast for thousands if not hundreds of thousands of time series. The problem we will explore together is: how can we get probabilistic forecasts that embrace uncertainty and scale?
The talk is light-hearted, contains few math formulas, and is aimed at forecasting practitioners! If you are new to the topic of forecasting, you'll be able to follow! We take the time to pose the problems and develop deeper from there.