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Machine Learning

Machine Learning

Using a Human-in-the-Loop to Overcome the Cold Start Problem in Menu Item Tagging

Learn how we built a classification model quickly, cheaply, and at scale

Abhi Ramachandran
Machine Learning

Optimizing DoorDash’s Marketing Spend with Machine Learning

Learn how we utilized cost curves to automate our marketing campaigns at scale

Aman Dhesi
Backend Data Machine Learning

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

Learn the challenges of reducing network overheads with gRPC optimizations

ArbazKhan
Data Machine Learning

Meet Sibyl – DoorDash’s New Prediction Service – Learn about its Ideation, Implementation and Rollout

Learn how building a prediction service enables the utilization of ML models based on real-time data

Cody Zeng
Machine Learning

Improving Experimental Power through Control Using Predictions as Covariate (CUPAC)

Too much varience can reducing experimental power. Learn how we solved this problem with our new CUPAC method

Jeff Li Yixin Tang Jared Bauman
Machine Learning

DoorDash’s ML Platform – The Beginning

Learn how we increased the scalability and productivity of the data science team by building a machine learning platform

Param Reddy
Machine Learning

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 ...

Animesh Kumar Dawn Lu Sri Santhosh Hari
Backend Machine Learning

Next-Generation Optimization for Dasher Dispatch at DoorDash

Learn how we optimized dasher selection using data science

Holly Jin Josh Wien Sifeng Lin
Machine Learning

Organizing Machine Learning: Every Flavor Welcome!

Learn how we organized our ML team including its vision, values and organizational structure

Alok Gupta
Machine Learning

Personalized Cuisine Filter

The consumer shopping experience is a key focus area at DoorDash. We want to provide consumers an enjoyable shopping experience by providing the right recommendation to the right consumer at the right time for the right location. On our app, there are cuisine filters on the top of the explore page. We have built a ...

Max Li Xiaochang Miao
Machine Learning

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. Since all these decisions are made based on experiment results, it is critical for us to have an experiment framework with rigor and velocity. Over the last few years, we have ...

Yixin Tang Caixia Huang
Machine Learning

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. Although the majority of the experiments conducted at DoorDash are A/B tests or difference-in-difference analyses, DoorDash occasionally relies on a type of experimentation internally referred to as ...

Carla Sneider Yixin Tang

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