Uber's Data Science Strategy: People, Product Lifecycle, Platformization - AI Trends

"Uber is making decisions in real time at global scale, while needing to take into account local nuances of the marketplaces," explained Franziska Bell, Senior Data Science Manager on the Platform Team at Uber. "And, of course, we also want to incorporate the user preferences on the product." As a result, Uber has invested heavily in data science, and Bell outlined some of Uber's data science strategy last month at the AI World Conference & Expo in Boston. Uber employs hundreds of data scientists working across the company, and Bell reports constant efforts to, "increase the innovation and speed with which these data scientists move." To speed up the rate of data science at Uber, the company has taken a dual approach: first to maximize each step of the existing data science project life cycle, and second to commoditize data science by creating platforms applicable to multiple use cases that are transferable and reusable. Data science projects at Uber fall into four life cycle stages, Bell explained: data exploration, iterative prototyping, productization, and finally monitoring.