DoorDash Analytics Case Study Questions (Updated for 2025)

You must pass one to two case study interviews to land an analytics data science job at DoorDash. These interviews typically take place in the second round, following your initial screening, and they are designed to assess both your analytical skills AND your coding prowess.

To be more specific, analytics case studies test your ability to

• A) Formulate metrics for a hypothetical scenario
• B) Write SQL or Python code to retrieve those metrics.

DoorDash analytics case studies fall into three categories:

• SQL Analytics- DoorDash SQL case studies ask you to investigate a business problem AND write SQL code to produce relevant metrics.

• Product Metrics- This type of DoorDash case study focuses entirely on your product sense. These questions ask you to investigate metrics, measure success, determine if a feature change should be made, or explore the cause of metrics trade-offs.

• Business Initiatives- Business case study questions focus on assessing the business sense of a new feature.

Need some help? Prepare for DoorDash careers with this case study guide! Get tips, a video overview, and sample DoorDash analytics case study questions.

No matter the company, there is a specific framework to follow for data analytics case study interviews. The steps are as follows: Ask clarifying questions. Use the components of the question to make assumptions. Form a hypothesis. Provide metrics and perform data analysis. Propose a solution.

Always remember: Make your recommendations ACTIONABLE. Here are some specific tips for DoorDash analytics case studies:

• Consider DoorDash’s business model- There are three user bases: customers, merchants, and Dashers (delivery drivers). Learn the pain points for each of these user bases. Your answer will likely address these pain points.

• Familiarize yourself with the data— You won’t get the data before the interview, but you can use sample datasets to practice. Common datasets for DoorDash cases include information about delivery times, order value, tip value, driver profiles, and customer accounts.

• Ask for clarification— DoorDash case studies tend to be vague to start with. For example, why is there so much variation between order times and pickup? Practice asking clarifying questions for business problems related to DoorDash.

Check out the linked video walkthrough for a more contextualized example of how to approach analytics case studies, including a narrated coding deep dive. It is very similar to what you might face at DoorDash. While watching, keep in mind the tips above and how you would adjust your approach:

Now, we can look at real DoorDash case studies and how we can apply the approaches we have covered.

Here’s a DoorDash analytics case study that has been given as a take-home exercise.

Question:

Analyze the provided data and generate specific recommendations for improving our business. Provide any supporting analysis and state your assumptions in your work.

The dataset includes information like:

• Customer order time
• Restaurant order time
• Driver arrives at restaurant time
• Order delivered time
• Driver ID
• Customer ID
• Amount of discount
• Amount of tip

How to Solve This Question:

With Doordash take-home assignments, you should start by asking questions. Then, you should perform your analysis and, ultimately, package that analysis and showcase your insights.

1. Start by Asking Questions

You can start with some broad questions like: What can I learn from this dataset, and what potential insights can I generate that would be of value? As you develop your questions, also think about specific metrics that you can pull to help answer them.

For example, you might have questions like:

• What can the timestamp components tell us about delivery efficiency?
• Does order value affect tipping?
• Which merchants generate the greatest volume of orders? Where are these merchants located?
• How efficient are Dashers? What are the average earnings of Dashers?

Develop your questions and remember to consider all three aspects of the Doordash business model: customers, Dashers, and merchants. Answering these questions, for example, would provide business insights affecting all three user groups.

Additionally, you should consider metrics. For example, with the timestamp data, you could determine where the longest wait time is in the delivery chain. You could also identify average tip values, average order size by merchant, average customer order value, etc.

2. Document Your Work

As you perform your analysis, provide examples of your work. Here’s a look at a Doordash analytics case study project on GitHub. The user provides visualizations and detailed SQL queries used in the analysis.

Here’s a look at a visualization from the project, showing the correlation between discount and tip amount:

3. Provide Clear Recommendations

Finally, you want to package your analysis and provide recommendations for the business. This is your chance to show off your business and product sense. Here are some example insights from the given dataset:

• Merchant Partnerships - Identify the top merchants from the data regarding average order value and daily orders. This data could used to reward these merchants through promotions or advertising and strengthen partnerships with these high-growth stores.

• Operational Efficiency - Identify the longest wait times in the delivery chain. This insight helps identify potential breakdowns in the delivery chain. How can you use this information to reduce delivery times?

• Average Wages for Dashers—The provided data shows the average tip amount and daily tip amount earned per driver. This can be used as a marketing tool to encourage Dasher sign-ups.

These are just a few of the insights you could generate. Hopefully, they illustrate what direction you can take in your analysis. Ultimately, your goal should be to provide compelling recommendations that showcase your ability to work with data and your business sense.

Here are two more DoorDash interview questions that can help you practice and refine your approach for your upcoming Doordash interview:

1. Let’s say you’re working on Doordash demand-side deliveries. Doordash is launching delivery services in New York City and Charlotte and needs a process for selecting Dashers (delivery drivers).

Important starting question: Are the operating conditions the same in both cities and how to do those impact deliveries? How would we go about deciding which Dashers are assigned deliveries?

How to Solve This Question:

Different cities mean different conditions within which to operate. For example, consider the urban density of the two markets in terms of mode of transportation. In New York City, where the buildings and restaurants are packed together tightly, Dashers may rely on bicycles or electric scooters. In a sprawling city like Charlotte, where restaurants and homes are further apart, a Dasher would likely use a car.

As a result of how Dashers navigate their city, you would want to adjust your thinking for the unique traffic patterns and realistic delivery ranges that impact delivery times.

Next, when it comes to how to choose Dashers to be assigned orders, think algorithmically. What inputs and outputs might be used in the model? For example, in using a Dasher for a particular order, we have a feature input such as:

• # of outstanding requests
• # distance to restaurant and customer
• # of available couriers on the road
• # of successful deliveries
• Dasher ratings

You can then measure these against output variables like customer satisfaction or actual completion time versus estimated completion time (on-time vs. late deliveries). However, it would likely result in some sort of bias that has to be accounted for.

Again, this isn’t a traditional analytics case study because you don’t have a dataset to analyze. But if you had a dataset for this type of question, you might think about metrics like:

• Average delivery time
• # of orders that were within the estimated delivery time
• Customer satisfaction score
• % of on-time deliveries

2. How would you determine whether telling the dashers to go online to meet delivery demands is effective?

Here’s a featured answer from one of our community members, pdmhkr20:

• Clarifying Question:

When “online” is mentioned, does it refer to a notification sent to all drivers on their app, regardless of whether they are online or offline? Or is it specifically to alert drivers who are offline so that, based on their availability, they can decide whether to take up deliveries?

• Assumptions:

Assuming that this feature allows more drivers to deliver food to customers, it would lead to reaching more customers, which in turn would generate more orders and, thus, more revenue. Therefore, it is assumed that increasing the user base is the key driver for the success of this feature, and the analysis will be based on this assumption.

• Analyzing User Flows:

DoorDash has three types of users: ***customers, *** dashers, and *** merchants. The impact of the feature change on all these segments will be analyzed to understand its effect on the entire ecosystem.

Firstly, based on the number of customer orders, which represents the demand, push notifications or emails will be sent to dashers, asking them to go online to meet the demand and thereby increase the supply. The dashers see the notifications, accept the deliveries, go to the restaurant, pick up the order, and deliver it to the customers.

From the restaurant’s perspective, they can accept more orders with more dashers available.

From the customers’ viewpoint, there will be fewer order cancellations.

Examine exactly how the product works. How does a user access a certain feature? How does a user use a certain feature? What kinds of different users are there?

• Define Metrics:

Based on the user flows mentioned, the following metrics can be defined for the three groups:

Dasher:

• Number of deliveries fulfilled per hour
• Average distance traveled
• Dasher Volume: Number of dashers available per hour
• Time spent on the app after receiving the notification

Merchants:

• Number of orders accepted

Customers:

• Number of orders
• Value of the orders
• Customer rating
• Number of canceled orders per customer
• Time between order placement and order receipt

Since the main interest is increasing customer orders on the platform and retaining customers, the key metrics to determine if the feature is working properly are average delivery times per hour and the number of customer orders.

• Test Hypothesis or Actionable Recommendations:

To test the hypothesis, group the dashers who went online during peak hours after receiving the notification and compare them with those who did not go online. Analyze if the delivery times decreased as the number of drivers increased.

Conduct A/B testing to validate the results.

3. Let’s say you work as a data scientist at DoorDash. You are tasked to build a machine-learning system that minimizes missing or wrong orders placed on the app. How would you go about designing this system?

Here’s a featured answer from one of our community members, poised-gold-tahr:

• Confirming Goal

minimizing wrong orders before users place or when users are placing the orders?

• Model Target

Order cancellation rates can be accessed via self-service cancellation or by contacting support to cancel an order. Since this is a binary variable, a binary classification model is applicable.

• How do Users Interact with the System

a banner/in-app notification when a wrong order is predicted, when the user lands on the restaurant page, and when the user is at check-out.

• Reasons for Wrong Order

1. wrong address (when GPS location does not agree with the user-selected delivery address)
2. distance too far away (for pick up orders)
3. quantity too large
4. delivery time/schedule etc.

• Features to Collect

past app interactions:

1. customer past orders
2. customer past cancellation and cancellation reason geolocation:
3. distance between customer geolocation & merchant wrong delivery time:
4. merchant hour (open/not open)/type of food (i.e., late night food/breakfast etc.) order features
5. number of items
6. subtotal of order

we should balance between precision and recall. In this case, wrong orders will cause customer/merchant/dasher dissatisfaction if we fail to detect (FN) & if we notify users too often when it’s not the wrong order (FP), they might abort the checkout process. This is also dependent on the intervention we build. [For example, instead of a notification at the checkout page, we might want to ask users if they need to update their delivery address when they open the app and are hundreds of miles away from their usual delivery address. In this case, we may care more about recall]

Supposing we build the prediction model to predict wrong orders when users are on the checkout page, optimizing for precision, and we pop up a window/in-app notification to ask the user to confirm the order again. We should perform offline testing and AB testing to determine effectiveness before launching.

• AB Testing

after offline testing, we should design an AB test & roll-out plan.

1. prereq: unit of randomization: customers (for consistent user experience); targeting - registered customers who placed at least one order; variant: test (in-app notification at checkout) control (default checkout experience); confirm goal and metric of ethe experiment - primary KPI is order cancellation rat,e and guardrail is revenue or number of orders
2. power analysis to determine proper experiment duration based on prior traffic & design roll-out
3. evaluation: a. monitor model precision/recall in production (this can be seen in the control group where no intervention was done) b. evaluation based on primary KPI and guardrails
4. launch decision

If you have a DoorDash data science interview coming up, practice with these resources from Interview Query:

• 500+ Data Science Interview Questions
• Product Sense Data Science Interview Course
• DoorDash Data Scientist Interview Guide
• Data Science Projects