DoorDash Data Scientist Interview Guide

tl;dr

DoorDash empowers local economies by connecting local businesses directly to consumers with just a few app clicks. Since launching in 2013, they have accelerated to 37,000,000+ monthly active users globally and a majority market share of food delivery service in the US.

DoorDash’s business model is optimized for their three-sided marketplace (customers, merchants, and dashers), which offers plenty of data points for data scientists to analyze. Case studies based on real DoorDash business problems are common throughout the data science interview process; 66% of the interview rounds include case studies to put your analytical skills to the test.

To ace your data science interviews with DoorDash, you’ll need excellent communication skills and the ability to think on your feet, coupled with solid experimentation and product knowledge. Discussions are fast-paced, and interviewers aren’t afraid to interrupt you with follow-up questions as you work through your case studies.

What does a DoorDash Data Scientist do?

DoorDash Data Scientists use quantitative analysis to break down large datasets to generate meaningful business insights and recommendations.

Data scientists at DoorDash analyze data generated from the company’s large user base and evaluate that data through the lens of the three-sided marketplace. To succeed, you have to be able to look at the same data funnels from the perspectives of consumers, restaurants, and dashers. To improve their user experience and add to their delivery offerings, much of the work revolves around making product enhancements and reducing friction.

Compensation packages at DoorDash are competitive and often ranked atop the industry. Note the average compensation for three levels of their data science function:

• L4 (DS1): ~$250K
• L5 (DS2): ~$300K
• L6 (DS3): ~$400K

Before you apply

1. Level up your product knowledge, review DoorDash’s current offerings, and learn about their three-sided marketplace.
2. Don’t miss any blind spots; prep for your interview with Exponent’s Data Science Interviews course.
3. Check out some of the top interview questions asked during DoorDash interviews.

Interview process

Case studies: Wash. Rinse. Repeat. DoorDash interviewers are interested in seeing how you respond to case study scenarios, especially when given a broad scope with little direction. After the recruiter screen, you can expect a case study in just about every interview round, so be prepared to analyze, ask questions, and define experiments.

Case studies aside, DoorDash’s hiring process for data scientists is pretty standard and is split into three stages:

1. Initial recruiter screen to get a general sense of your experience and culture fit
2. Technical screen consisting of a coding component and a case study
3. Final round of four interviews (split between a cross-functional stakeholder, the hiring manager, and two other data scientists)

The most unique aspect of DoorDash’s hiring is their team matching process. You’re matched to a team after the technical screen, allowing interviewers to see some of your skills in action before deciding where you’ll have the most impact. Teams you might land on include Consumer and Growth, Business Operations, or New Verticals (to name a few).

1. Recruiter screen

First, you’ll go through a typical recruiter screen. A DoorDash recruiter gives an overview of the company and the role, explains the interview process and what to expect, and asks basic behavioral questions.

The recruiter asks questions like:

• What interests you about working at DoorDash?
• Tell me about yourself.

2. Technical screen

The technical screen is a 60-minute interview divided evenly into two parts: coding and a case study. The first half focuses on an SQL-based coding assessment. The interviewer provides you with tables and asks four questions that increase in difficulty as you progress. DoorDash models their SQL questions (in style and difficulty) to those asked at FAANG companies.

The company doesn’t care about your process or your ability to explain your approach for the SQL portion of the round - they solely want to see whether you can come up with the correct answers.

Topics to study for this portion of the round include:

• Window functions like DENSE_RANK
• CTEs
• Joins

After the coding assessment, you’ll spend 30 minutes on your first case study. The interviewer gives you a very high-level problem statement like:

• Some consumers were unhappy after receiving cold food. How can we solve this problem?

Interviewers judge your ability to ask clarifying questions, extract and break down key points, provide metrics for experimentation, and identify guardrails for your experiments. Since you’re not given structure in the question, you’ll have to provide structure in your response (in a way that’s easy to follow.) Unlike the SQL portion, this is where interviewers want to understand your process. Expect them to ask how you know your metrics are meaningful. For bonus points, emphasize aspects of the three-sided marketplace.

You’ll have the most success in this portion of the round—and for the other case studies—if you brush up on:

• Modeling and experimentation fundamentals, from ideation to testing
• A/B testing (a must-know for this round)
• P-values
• Segmentation
• Time series analytics

3. Final round

The final round consists of four interviews with:

• A cross-functional teammate (called a “business partner interview”)
• A data science manager from another data science team
• A data scientist
• The hiring manager

The business partner interview is behavioral, while the other three are more technical and case study-focused. The case studies are similar across all the interview rounds. They give a realistic, high-level problem statement and then let you guide the discussion by asking questions and explaining your methods.

Interview questions

Coding

You’ll only see coding questions during the SQL portion of the technical screen, so that they won’t take up a significant amount of your total interview time. Candidates reported question models that followed FAANG standards. You have 30 minutes to answer four questions; the only thing interviewers are assessing here is whether you can deliver the correct answers.

Expect questions like:

• Given the order table, return the top customer_id per month.
• Given the order table, return the top customer_id outside of high-frequency customers (30+ orders per month).
• Return the sales associated with high-frequency customers and show these as a percentage of all sales.

Behavioral

You’ll tackle light behavioral questions during the recruiter screen and more in-depth during your business partner interview in the final round. These questions assess your past projects and how well you work with others. Expect questions like:

Why do you want to work at DoorDash?

Asked at DoorDash 

View 2 answers

• Tell me about one of your favorite projects.
• How do you work with non-technical stakeholders?
• How do you prioritize your work?
• How do you handle differences in opinions?

Case studies

You’ll deal with case studies in four out of your six total interview discussions, so this is where you should spend the bulk of your preparation efforts. The case study problem statements usually require solid experimentation knowledge, particularly A/B testing.

As you move through the case studies, interviewers will ask you follow-up questions to probe into your thought process. They may even interrupt your workflow and try to catch you off-guard. They’re critical of every word and want to see you express exact responses, so don’t mince your words during these interviews. Candidates succeed in the case studies when they’re confident in their experimentation methods and have good background knowledge of the product.

In the final round, each data scientist interviewer gives you a case study. The data scientist manager will likely give you a problem statement related to the team they work on. For example, if the manager works on the Ads team, you might be asked something like:

• How would you measure whether the current ad product is effective?

The second data scientist interview in the final round is with a potential colleague - a peer from your matched team. They’ll ask another case study question that tests your product intuition and analytical capabilities. For this round, you might see a question like:

• How would you determine if a newly launched product, like in-app store search, is working?

Finally, the hiring manager for your matched team will present one or two more case studies. Since the case studies don’t necessarily increase in difficulty (across rounds), DoorDash seeks consistency across your answers. Can you consistently approach similar types of problem statements with the same level of consideration, detail, and analysis?

For this round, expect more product analytics-based questions like:

• How can you justify investing more resources (e.g., headcount) into our product?
• How can you optimize the user experience from the “store page” to “checkout?”

Additional resources

• Use Doordash’s app to learn about their 3-sided marketplace (consumers, dashers, & merchants.)
• Browse the data-centric posts on DoorDash’s engineering blog.
• Get actionable feedback from experienced data science interviewers.
• Check out a top-rated data science interview course to brush up on your tablestake skills.

FAQs

How should I prepare for a DoorDash Data Scientist interview?

DoorDash interviewers will grade you based on your communication, problem sense, and analytical abilities.

Their interview process prioritizes case studies. Sharpen your ability to ask clarifying questions and explain your methods and reasoning. Research DoorDash’s product funnel and offerings and emulate the user experience from all three sides of their marketplace. Study up on experimentation fundamentals, especially A/B testing and time series analysis.

How much do DoorDash Data Scientists make?

DoorDash offers fairly competitive compensation packages. Their leveling system supports junior-level data scientists through staff data scientists, with total compensation packages ranging from $225K to $429K on average.

How long is the DoorDash Data Scientist interview process?

The length of the interview process varies slightly depending on the role and location, but generally, you can expect to complete the interview loop in two to four weeks.