To land an analytics data science job at DoorDash, you have to pass one to two case study interviews. 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
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? The following DoorDash case study guide provides everything you need to prepare! We have 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 aren’t going to get the data before the interview, but you can use sample datasets to practice. Commonly, datasets for DoorDash cases will 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. For example: Why is there so much variation between order times and pickup? Practice asking clarifying questions for business problems related to DoorDash.
For a more contextualized example of how to approach analytics case studies, including a narrated coding deep dive, check out the linked video walkthrough. 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.
Analyze the provided data and generate some specific recommendations on how our business can improve. Provide any supporting analysis and state your assumptions in your work.
The dataset includes information like:
With Doordash take-home assignments, you should start by asking questions. Then, 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 be thinking about specific metrics that you can pull to help answer them.
For example, you might have questions like:
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 of these user groups.
Additionally, you should be thinking about metrics. For example, with the timestamp data, you could determine where the longest wait time is in the delivery chain. You could 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, as well as 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 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 in terms of average order value and orders per day. This data could used to reward these merchants through promotions or advertising and strength 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 - With the provided data, you could find the average tip amount, daily tip amount earned per driver, etc. This can be used as a marketing tool to encourage Dasher sign-ups.
These are just a few of the insights you could generate. But hopefully, it illustrates 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’s another DoorDash interview question, specifically a delivery driver business case. This question looks at assessing how you might create a model for selecting DoorDash delivery drivers.
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 does that impact deliveries? How would we go about deciding which Dashers are assigned deliveries?
Different cities mean different conditions to operate within. For example, think about the urban density of the two markets on 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:
You can then measure these against output variables like customer satisfaction or actual completion time versus estimated completion time (on-time vs late deliveries), though 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: