DoorDash Data Scientist Interview Guide

Interview Guide

Detailed, specific guidance on the DoorDash Data Scientist interview process - with a breakdown of different stages and interview questions asked at each stage

Everything you need to win this interview

The role of a DoorDash Data Scientist

DoorDash Data Scientists, much like DSes across pretty much all major tech companies, work across teams to uncover insights and translate data into actionable recommendations that guide company decisions. They analyze large-scale datasets (e.g. customer orders, merchant info, deliveries) to find patterns, forecast demand, and optimize operations and growth strategy.

DoorDash employs Data Scientists in many domains (pricing, logistics, growth, etc.). For example, a DS on the Pricing team builds demand-forecast and dynamic-pricing models to optimize fees and promotions. Another DS in Customer Growth might analyze marketing campaigns and consumer behavior to improve user acquisition. On the Dasher/logistics team, DS roles include optimizing real-time delivery dispatch and routing using ML and optimization techniques. For instance, DoorDash’s dispatch engine (‘DeepRed’) uses ML models plus optimization to assign orders to Dashers efficiently. Lots of companies have DS roles with similarly high-scale, high-challenge scopes (have a look at the Facebook Data Engineer and Meta DS Initial guides for references to their interview processes).

DoorDash Data Scientists partner with data engineers and platform teams to ensure reliable data pipelines and quality data. They help define requirements (e.g. instrumentation or ETL logic) so that analytics and models run on accurate, up-to-date data.

DoorDash Data Scientists also regularly collaborate with product, marketing, sales, and ops teams to ensure analytics align with business needs. In other words, they “turn data into relevant recommendations” that drive decisions across the company. Clear communication of insights (e.g. via dashboards or presentations) is highly valued and encouraged.

DoorDash hires data scientists at multiple levels (e.g. DS0/E3 up to DS3/E6) in various analytics teams. It also employs Senior Data Scientists, Data Science Managers (who lead DS teams), and related roles (e.g. Data Analyst, ML Engineer) in its analytics organization.

Positions open on DoorDash’s careers page often change; candidates should regularly monitor DoorDash’s official careers site and LinkedIn for the latest openings.

Skills and Qualifications needed for Data Scientists at DoorDash

Some of the skills and qualifications that may be required for a Data Scientist at DoorDash include:

  • A strong academic background in a field such as computer science, mathematics, statistics, or physics will give you a solid foundation in the concepts and techniques used in data science.
  • Develop strong coding skills, specifically in Python and SQL. These are the most commonly used languages by data scientists at DoorDash and will be essential for working with large datasets and building models.
  • Familiarity with statistical and data mining techniques will help you to build and interpret models and make data-driven predictions.
  • Experience with big data tools like Hadoop, Spark, and Hive. These tools will allow you to work with large datasets and perform complex data analysis tasks.
  • Knowledge of data visualization tools like Tableau or Power BI will allow you to effectively communicate your findings and insights.

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DoorDash Data Scientist Interview Guide

As a part of the DoorDash Data Scientist interview, the candidate will need to go through multiple interview rounds:

1. Recruiter Screen - A 30–45 min chat to review your resume/background.

2. Technical Screen - A 60-min call (often with two parts) involving SQL or Python coding and a data/product case. These are usually SQL or Python questions with medium complexity. You can use the internet or documentation; this reflects DoorDash’s real work style. So feel free to check in with them or ask them if you need help and they'll usually give you the go-ahead to do this. In some cases, this round can also consist of a rapid-fire of SQL questions.

3. Onsite Rounds - Multiple interviews (usually 4-6) including more SQL/analytics problems, a deeper product/business case, and behavioral questions. For example, you might be asked to design an A/B test for a DoorDash feature or analyze sample data on the spot. The goal of this round is to understand your technical depth and breadth alongside the knowledge about DoorDash' business model, and the role you might play within this ecosystem.

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Check out video guide that delves into the interview process and provides valuable tips tailored to each round of the interview.

Relevant Guides

DoorDash Data Scientist: Recruiter Screen

Overview

During the recruiter screening, the focus is typically on assessing if your abilities align with the position being applied for. This typically includes informal queries about your experiences and qualifications. The goal of this session is to provide the recruiter with a deeper understanding of your background and to assist you in understanding the role. When prompted with the question, "Tell me about yourself," we’d recommend highlighting key points or strengths that can leave a positive impression on the interviewer and increase the chances of advancing to the next round.

Interview Questions

  • Why do you want to join DoorDash?
  • Why do you think you will be a good fit for the role?
  • What responsibilities do you expect to have from your job at DoorDash?
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DoorDash Data Scientist: Coding Round

Overview

The SQL round usually includes medium-difficulty analytic queries. Expect questions on GROUP BY, window functions, joins, and date/time manipulation. Candidates report that DoorDash allows use of online resources/documentation during this round, mimicking on-the-job conditions. Interviewers may also ask several quick SQL puzzles. Make sure to practice writing efficient SQL for analytics tasks, as these skills are crucial. Some candidates reported facing Senior Data Scientists and the key was to be quick and accurate with your solutions. Additional preparation resources include the Netflix Data Scientist guide, since Netflix HMs also ask similar questions.

Interview Questions

  • Write a SQL query to find the top 10 customers who placed the most orders by total order value, including their order count and total order value, and join the data with the customer's name, address, and email from the customer table.
  • Write a SQL query to find the total number of orders placed in the last 30 days, group the results by day, and order the results by date in descending order.
  • Write a SQL query to find the average order value for each product category and show the top 3 categories with the highest average order value.
  • What are SQL window functions?
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DoorDash Data Scientist: Business Intuition Interviews

Overview

The final round(s) is a product/business case interview. Interviewers assess your understanding of DoorDash’s multi-sided marketplace and metrics. Be prepared to discuss DoorDash’s business model (customers, merchants, Dashers), relevant KPIs (e.g. take-rate, GMV, order completion rate), and how you would use data or experiments to improve the platform. Some candidates reported facing a technical screening along with the business case study similar to the previous round. For example, one might ask how you’d measure engagement, or design an A/B test for a new feature. Candidates report questions on multi-sided dynamics and metrics often come up.

Please note that this round is role specific and you might encounter a different case depending on the role you are applying for.

Interview Questions

  • How do you analyze if a product is successful?
  • What are the most important metrics for DoorDash?
  • How do you measure revenue and cost?
  • How do you capture customer satisfaction when there is a lack of survey responses?
  • How do you measure customer engagement and disengagement?

How to Apply for a Data Scientist Job at DoorDash?

To apply for a Data Scientist job at DoorDash, you will need to visit the DoorDash career website and search for open Data Scientist positions. Once you have found a position that you are interested in, you will be able to submit an application online. However, we would highly recommend taking the referral route if you know someone in the company as it increases your chances meaningfully. One tip regarding your resume - make a few tweaks for the position and the role you are applying for which will help you have a better chance compared to other candidates. For instance, some DS roles might explicitly call for a background in ML; others might need you to be very good at visualization - these are exactly the sort of things you should then highlight if you have past experience doing.

DoorDash Data Scientist Salary

DoorDash Data Scientist Salary in US ranges from ~$225K–$450K for levels E3 to E6 with the Median total comp ~$300K. For eg: E3 (DS0) ~ $225K total (≈$169K base + stock), E5 (DS2) ~ $274K total, E6 (DS3) up to ~$450K total. Base salaries generally span ~$160K (junior) to $250K+ (senior). Bonuses/equity add significantly. These figures exclude benefits. You can find the latest figures from Levels.fyi and Blind.

DoorDash DS Interview Preparation Plan

  1. Research DoorDash’s Business: Review DoorDash’s marketplace model (customers/merchants/Dashers). Read recent news or engineering blogs (e.g. new products, generative AI projects) to understand the context and mission
  2. SQL and Analytics Practice: Master SQL for analytics (GROUP BY, window functions, joins, date math). Practice on datasets (e.g. restaurant orders) and time-based analysis, as DoorDash emphasizes these skills. Read more about this topic on this blog post by Kunal Shah a tech lead who'd been at DoorDash for ~4 years (also ex-Google, Meta, Airbnb).
  3. Case Studies & A/B Testing: Prepare for business-case questions by designing metrics and experiments. For example, practice answering “How would you test a change to the restaurant ranking algorithm?” using A/B test logic. Many DoorDash interviewers emphasize the multi-sided model and expect discussion of metrics; in fact DoorDash even has a blog on experiment design (A/B tests) for that ecosystem.
  4. Machine Learning & Stats Review: Brush up on key DS topics (e.g. forecasting, classification) and causal inference (e.g. using control metrics). Understand how to evaluate models (precision, ROC, etc.) and discuss trade-offs.
  5. Mock Interviews: Use practice interviews to simulate both SQL coding questions and product/business cases. Review answers with the right coach, focusing on communication, and case presentation. Prepfully’s mock DS interviews (with a DoorDash coach) can help spot gaps.
  6. Behavioral Prep: Prepare stories using the STAR method for leadership, teamwork, and challenges. Highlight any experience leading data projects (essential for manager roles) and collaborating cross-functionally.
  7. Company-Specific Prep: Study DoorDash’s tech stack (e.g. Snowflake, Kafka) and any public engineering content (Fabricator, dispatch). This shows genuine interest. Also be ready to discuss metrics that matter to DoorDash (order volume, completion rate, spend, etc.)

Conclusion

The interview process for a Data Scientist role at DoorDash typically includes 3 primary rounds - a recruiter screen, a coding round, and the final business case study. During the recruiter screen, the interviewer will assess your qualifications, experience and alignment with the role. The coding round will focus on your data science and programming abilities, especially your SQL skills. The final interview will be a business intuition round which will comprise a live business case study on a product along with some behavioral questions. The goal of this round is to understand your depth and breadth of knowledge about DoorDash' business model, and the role you might play within this ecosystem. In summary, focus your prep on SQL and analytics skills, business metrics & case studies, and DoorDash’s marketplace context. Stay data-driven and communicate your recommendations clearly. Good luck!

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Responsibilities of a Data Scientist at DoorDash

The responsibilities of a data scientist at DoorDash across roles can broadly be seen as-

  • Design and maintain data pipelines and infrastructure (pipelines, databases, ETL) that supports DoorDash's data science efforts, including data collection, storage, and protection.
  • Identify and track key business metrics; build dashboards/reports (using SQL, Looker/Tableau, etc.) to monitor them.
  • Develop and build statistical/ML models for business use-cases. For instance develop and implement statistical models that can predict customer behavior and inform pricing strategies, optimize delivery routes and improve the overall efficiency of the company's logistics.
  • Validate and iterate on models: monitor performance (offline and in production) and refine models as needed.

If you’re interviewing for a Data Science Manager role - you will need to index heavily on the Leadership and People parts alongside core craft skills. You’d be responsible for leading a team of Data Scientists, Data engineers and data analysts - and the end to end strategy and delivery around the data domain you own.

Frequently Asked Questions