In this post, we introduce a method we call CUPAC (Control Using Predictions As Covariates) that we successfully deployed to reduce extraneous noise in online controlled experiments, thereby accelerating our experimental velocity. 
Rapid experimentation is essential to helping DoorDash push key performance metrics forward.
DoorDash uses Machine Learning (ML) at various places like inputs to Dasher Assignment Optimization, balancing Supply & Demand, Fraud prediction, Search Ranking, Menu classification, Recommendations etc.
DoorDash is a dynamic logistics marketplace that serves three groups of customers:
Merchant partners who prepare food or other deliverables,
Dashers who carry the deliverables to their destinations, 
Consumers who savor a freshly prepared meal from a local restaurant or a bag of groceries from their local grocery store. 
For such a real-time platform as DoorDash, just-in-time insights from data generated on-the-fly by the participants of the marketplace is inherently useful to making better decisions for all of our customers.
At DoorDash, our logistics team focuses on efficiently fulfilling high quality deliveries.
DoorDash’s principles and processes for democratizing Machine Learning
Six months ago I joined DoorDash as their first Head of Data Science and Machine Learning.
The consumer shopping experience is a key focus area at DoorDash.
Within the dispatch team of DoorDash, we are making decisions and iterations every day ranging from business strategies, products, machine learning algorithms, to optimizations.
At DoorDash, we believe in learning from our marketplace of Consumers, Dashers, and Merchants and thus rely heavily on experimentation to make the data-driven product and business decisions.
Overview
Introduction
What is the assignment problem at DoorDash?
In May, DoorDash participated at the O’Reilly Artificial Intelligence Conference in New York where I presented on “How DoorDash leverages AI in its logistics engine.” In this post, I walk you through the core logistics problem at DoorDash and describe how we use Artificial Intelligence (AI) in our logistics engine.
