How forecasts and mathematical optimization are used in last mile logistics industry. — At DoorDash, we want our service to be a daily convenience offering timely deliveries and consistent pricing. Achieving these objectives requires a good balance between the supply of Dashers (our term for delivery drivers) and the demand for orders. During periods of high demand we generally increase pay, providing an…

An overview of variance reduction techniques for improving the power of your A/B tests — In graduate school, I attended a session on reinforcement learning behavior, specifically on topics related to laboratory rats and experimentation. One thing that struck me about the results of the talks is that there was almost no variability in experimental conditions. The rats in these experiments were inbred to have…

A company that follows an ad-hoc process when doing Analytics generally suffers from the following issues: Fudgeable (aka 💩) analyses, where the final outcome of a report is a series manual and inherently irreproducible steps. People spending countless hours arguing about who has the right numbers due to the inability…

or how to fail fast in Python — Imagine the following two scenarios. Scenario 1: You deployed a machine learning RESTful service that provides disease diagnoses. Unfortunately, you might have certain conditions where you know your model should not be invoked because of certain regulatory constraints (e.g., some countries might not allow automated patient diagnosis unless some pre-requisites…

Machine learning algorithms are increasingly used to make decisions around assessing employee performance and turnover, identifying and preventing recidivism, and assessing job suitability. These algorithms are cheap to scale and sometimes can be cheap to develop in an environment filled with good quality research and tooling. Unfortunately, one thing these…