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Machine Learning

Machine Learning

How DoorDash Upgraded a Heuristic with ML to Save Thousands of Canceled Orders

Learn how DoorDash engineering utilized ML models to o accurately track Merchants' operational status and ability to full fill orders.

Slava Nikitin Gabriel Lerner Machine Learning

Selecting the Best Image for Each Merchant Using Exploration and Machine Learning

In order to inspire DoorDash consumers to order from the platform there are few tools more powerful than a compelling image, which raises the questions: what is the best image to show each customer, and how can we build a model to determine that programmatically using each merchant’s available images? Out of all the different ...

Chun-Chen Kuo Machine Learning

Augmenting Fuzzy Matching with Human Review to Maximize Precision and Recall

When we encountered a business problem that required an error rate of zero, we turned to a classification model with human review

Robert B. Kaspar Mitchell Koch Machine Learning

Homepage Recommendation with Exploitation and Exploration

Learn how our model balances exploitation and exploration during ranking to optimize the consumer experience while simultaneously improving fairness for merchants

Yu Zhang Data Machine Learning

Five Common Data Quality Gotchas in Machine Learning and How to Detect Them Quickly

Data preparation, represents The vast majority of work in developing machine learning models, learn how to make things easier

Kornel Csernai Devjit Chakravarti Machine Learning

Evolving DoorDash’s Substitution Recommendations Algorithm

When expanding from made-to-order food delivery to new product verticals like groceries, convenience, and retail, new challenges arise, including how to ensure inventory will be available to fulfill orders. As a business, we always want customers to receive all the items they ordered. For restaurant orders, this is easy to do because merchants offer relatively ...

Dawn Lu Machine Learning

4 Essential Steps for Building a Simulator

For complex systems simulating the impact of algorithmic changes is often faster and less costly than experimenting on features in production

Devjit Chakravarti Machine Learning

Leveraging Causal Inference to Generate Accurate Forecasts

Learn how DoorDash captures hard to measure macroeconomic effects like IRS refunds and the effect of Daylight savings in these case studies

Chad Akkoyun Qiyun Pan Data Machine Learning

Meet Dash-AB — The Statistics Engine of Experimentation at DoorDash

Learn how DoorDash was able to test uniformly according to established best practices and reuse complex statistical methods with Dash AB

Caixia Huang Yixin Tang Machine Learning

Building the Model Behind DoorDash’s Expansive Merchant Selection

Having a quality selection did not happen by accident. Learn about the ML models that power the diverse, high quality selection on our platform

Lu Wang Ying Yang Chen Dong Machine Learning

Using Gamma Distribution to Improve Long-Tail Event Predictions

Which loss function is best for long tail event prediction? learn how we used gamma distributions to boost eta prediction accuracy

Pratik Parekh Zhe Jia Machine Learning

Using a Multi-Armed Bandit with Thompson Sampling to Identify Responsive Dashers

Epsilon-Greedy, The Upper Confidence Bound, Thompson Sampling: which is the best Multi-armed bandit algorithm for promotion optimization?

Arjun Sharma

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