Data | DoorDash Engineering Blog

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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

11 Minute Read

Data Machine Learning

Running Experiments with Google Adwords for Campaign Optimization

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 ...

Building Flexible Ensemble ML Models with a Computational Graph

DoorDash extended its machine learning platform to support ensemble models.

2020 Hindsight: Building Reliability and Innovating at DoorDash

DoorDash recaps a number of its engineering highlights from 2020, including its microservices architecture, data platform, and new frontend development.

The Undervalued Skills Candidates Need to Succeed in Data Science Interviews

After interviewing over a thousand candidates for Data Science roles at DoorDash and only hiring a very small fraction, I have come to realize that any interview process is far from perfect, but there are often strategies to improve one’s chances . Over the course of our interviews, I’ve come across some great candidates who ...

Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression

When a company with millions of consumers such as DoorDash builds machine learning (ML) models, the amount of feature data can grow to billions of records with millions actively retrieved during model inference under low latency constraints. These challenges warrant a deeper look into selection and design of a feature store — the system responsible ...

Uncovering Online Delivery Menu Best Practices with Machine Learning

Learn how we analyzed over 100K online delivery menus to develop menu best practices

Hot Swapping Production Tables for Safe Database Backfills

DoorDash engineering explains how to edit large data tables safely and quickly in a production database.

Improving Online Experiment Capacity by 4X with Parallelization and Increased Sensitivity

To speed up the development of new features we needed a way to increase our experiment capacity. Learn how we improved it by 4X

Integrating a Search Ranking Model into a Prediction Service

Learn how moving ML models to a prediction service can free up RAM and CPU for more scalable development

How DoorDash is Scaling its Data Platform to Delight Customers and Meet our Growing Demand

Learn the challenges and best practices to successfully growing a data platform organization

Supporting Rapid Product Iteration with an Experimentation Analysis Platform

DoorDash engineers built Curie, a new experimentation analysis platform, to better gauge the success of product experiments.

Enabling Efficient Machine Learning Model Serving by Minimizing Network Overheads with gRPC

Learn the challenges of reducing network overheads with gRPC optimizations