AI & ML Archives - Page 7 of 8

Supercharging DoorDash’s Marketplace Decision-Making with Real-Time Knowledge

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.

Next-Generation Optimization for Dasher Dispatch at DoorDash

At DoorDash, our logistics team focuses on efficiently fulfilling high quality deliveries.

Organizing Machine Learning: Every Flavor Welcome!

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.

Personalized Cuisine Filter

The consumer shopping experience is a key focus area at DoorDash.

Analyzing Switchback Experiments by Cluster Robust Standard Error to Prevent False Positive Results

Within the dispatch team of DoorDash, we are making decisions and iterations every day ranging from business strategies, products, machine learning algorithms, to optimizations.

Experiment Rigor for Switchback Experiment Analysis

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.

Reinforcement Learning for On-Demand Logistics

Overview

Introduction
What is the assignment problem at DoorDash?

How Artificial Intelligence Powers Logistics 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.

Personalized Store Feed with Vector Embeddings

Customers come to DoorDash to discover and order from a vast selection of their favorite stores, so it is important to be able to surface what is most relevant to them.

Switchback Tests and Randomized Experimentation Under Network Effects at DoorDash

To A/B or not to A/B, that is the question

Overview
On the Dispatch team at DoorDash, we use simulation, empirical observation, and experimentation to make progress towards our goals; however, given the systemic nature of many of our products, simple A/B tests are often ineffective due to network effects.