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Machine Learning

Machine Learning

Improving Subgroup Analysis with Stein Shrinkage

Learn how DoorDash Utilized Stein Shrinkage to perform subgroup analysis without the danger of high variances

David Kastelman Machine Learning

Using Triplet Loss and Siamese Neural Networks to Train Catalog Item Embeddings

Learn how DoorDash used neural networks to better understand the contents of its large online catalog and improve search and recommendations

Abhi Ramachandran Machine Learning

Increasing Operational Efficiency with Scalable Forecasting

Scaling forecasting to a large data team is not practical without a scalable platform. Learn how we built forecast factory at DoorDash

Ryan Schork Machine Learning

Using ML and Optimization to Solve DoorDash’s Dispatch Problem

DoorDash delivers millions of orders every day with our last-mile logistics platform. Look under the hood and learn how the platform works

Alex Weinstein Jianzhe Luo Machine Learning

Predicting Marketing Performance from Early Attribution Indicators

Learn how DoorDash managed to respond faster to changes in the market by building a model to utilize early attribution data from experiments

Zhe Mai Machine Learning

Managing Supply and Demand Balance Through Machine Learning

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

Stas Sajin Zainab Danish Data Machine Learning

Maintaining Machine Learning Model Accuracy Through Monitoring

Machine learning model drift occurs as data changes, but a robust monitoring system helps maintain integrity.

Swaroop Chitlur Kornel Csernai Machine Learning

Improving ETA Prediction Accuracy for Long-tail Events

Learn how we managed to better predict long tail delivery estimations using historical and realtime features as well as custom loss functions

Dawn Lu Pratik Parekh General Machine Learning

Best Practices for Regression-free Machine Learning Model Migrations

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 ...

Ying Chi Data Machine Learning

Building Riviera: A Declarative Real-Time Feature Engineering Framework

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 ...

Allen Wang Kunal Shah Machine Learning

Why Good Forecasts Treat Human Input as Part of the Model

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 ...

Brian Seo Data Machine Learning

How to Drive Effective Data Science Communication with Cross-Functional Teams

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 ...

James Williams Lokesh Bisht

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