This document discusses how to scale data science products rather than data science teams. It presents examples of common problems faced when scaling products and classifies them as either product design problems, software engineering problems, or mathy/machine learning problems. The key issues discussed include managing user expectations, maintaining many models, using shared code across customer bases, testing accuracy in new markets, addressing cold starts for unknown customers, and identifying feedback loops.