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.
Category Archives: AI & ML
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.
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.
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.
Building Flexible Ensemble ML Models with a Computational Graph
DoorDash extended its machine learning platform to support ensemble models.
Se buscan: Científicos de datos con brillantez técnica y sentido empresarial
DoorDash busca científicos de datos que prioricen los impactos empresariales de su trabajo.
Retrospectiva 2020: Creación de fiabilidad e innovación en DoorDash
DoorDash recapitula una serie de aspectos destacados de su ingeniería a partir de 2020, incluida su arquitectura de microservicios, su plataforma de datos y su nuevo desarrollo frontend.
Things Not Strings: Understanding Search Intent with Better Recall
For every growing company using an out-of-the-box search solution there comes a point when the corpus and query volume get so big that developing a system to understand user search intent is needed to consistently show relevant results.
We ran into a similar problem at DoorDash where, after we set up a basic “out-of-the-box” search engine, the team focused largely on reliability.
Iterating Real-time Assignment Algorithms Through Experimentation
DoorDash operates a large, active on-demand logistics system facilitating food deliveries in over 4,000 cities.
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.