Learn how DoorDash managed to respond faster to changes in the market by building a model to utilize early attribution data from experiments
Achieving DoorDash's objectives requires a good balance between the supply of Dashers and the demand for orders. Learn how we manage this with ML
Machine learning model drift occurs as data changes, but a robust monitoring system helps maintain integrity.
Learn how we managed to better predict long tail delivery estimations using historical and realtime features as well as custom loss functions
Migrating functionalities from a legacy system to a new service is a fairly common endeavor, but moving machine learning (ML) models is much more challenging. Successful migrations ensure that the new service works as well or better than its legacy version. But the complexity of ML models makes regressions more likely to happen and harder ...
In a business with fluid dynamics between customers, drivers, and merchants, real-time data helps make crucial decisions which grow our business and delights our customers. Machine learning (ML) models play a big role in improving the experience on our platform, but models can only be as powerful as their underlying features. As a result, building ...
At DoorDash, getting forecasting right is critical to the success of our logistics-driven business, but historical data alone isn’t enough to predict future demand. We need to ensure there are enough Dashers, our name for delivery drivers, in each market for timely order delivery. And even though it seems like people’s demand for food delivery ...
Analytics teams focused on detecting meaningful business insights may overlook the need to effectively communicate those insights to their cross-functional partners who can use those recommendations to improve the business. Part of the DoorDash Analytics team’s success comes from its ability to communicate actionable insights to key stakeholders, not just identify and measure them. Many ...
Running experiments on marketing channels involves many challenges, yet at DoorDash, we found a number of ways to optimize our marketing with rigorous testing on our digital ad platforms. While data scientists frequently run experiments, such as A/B tests, on new features, the methodology and results may not seem so clear when applied to digital ...
DoorDash extended its machine learning platform to support ensemble models.
DoorDash seeks data scientists who prioritize the business impacts of their work.
DoorDash recaps a number of its engineering highlights from 2020, including its microservices architecture, data platform, and new frontend development.
Susbscribe to the DoorDash engineering blog