DoorDash Data Scientist Interview Questions + Guide in 2024

Among the major food delivery players in the US market, DoorDash, with over 2 billion orders in 2023, holds the apex position. A major part of their success is attributable to data science and analytics. The data scientists at DoorDash help resolve decision-making challenges related to customer acquisition, fraud detection, marketing, and launches in new cities.

As someone planning to interview for the data scientist position at DoorDash, this guide is designed specifically for you. We’ll cover the interview process, the answers to common questions, and tips to help you gain an edge.

The interview usually consists of 3 to 4 rounds, depending on the seniority and experience required for the data scientist position. There is usually an initial telephone call with HR, followed by coding, case studies, and on-site interview rounds to evaluate your alignment with DoorDash’s technical and behavioral requirements for the role.

Submitting the Application

DoorDash recruiters frequently reach out to candidates on LinkedIn and other platforms, encouraging them to apply. Additionally, the latest open data scientist positions are available on the DoorDash Career portal, where you can review and apply for suitable data scientist roles.

While preparing your CV, tailor it to the job description and mention your technical, soft leadership, and communication skills, which are necessary for data scientists.

HR Interview Round

According to previous candidates, the shortlisted CVs have survived a rigorous screening process. If you’re among the selected, an HR representative from DoorDash will contact you and arrange a telephone interview.

While basic behavioral questions regarding your experience and values will probably be asked during this round, a few pre-defined SQL and case study questions may also be hurled to judge your preparedness and technical skill set.

Data Scientist Coding Round

If your answers to the HR department have been satisfactory, you’ll be invited to the first technical interview round. The coding round for data scientist candidates at DoorDash typically revolves around writing SQL queries and answering a few tangential questions. Machine learning and product metrics questions also are occasionally asked during this round.

In most cases, the hiring manager or a senior data scientist from the project takes the coding interview.

Business Intuition/Case-Study Interview

Success in the coding round allows you to advance to the next stage—the business intuition/case-study interview. Here, you’ll be assigned a take-home or dataset problem (analysis and SQL) to submit within 48 to 72 hours. A DoorDash data scientist will discuss your approach and solution to the submitted take-home assignment via a thorough review call.

In some cases, another machine-learning case study (concept and model building) might be assigned to evaluate your specific skillset regarding ML concepts.

Onsite Interview Round

The DoorDash data scientist onsite interview round lasts more than 5 hours, including a lunch break. You’ll be subjected to multiple interview rounds evaluating your cultural fitness and skills in SQL queries, system design, machine learning, and whiteboard coding.

You’ll meet potential colleagues and maybe even the hiring manager during the visit.

Your answers will be compared to those of other candidates before DoorDash informs you of their decision regarding your candidacy as a data scientist.

As a data scientist, DoorDash expects you to have technical proficiency in SQL queries, ETL, A/B testing, and analytical tools. You’ll also be evaluated for your ability to apply those skills in real-life scenarios, such as data presentations, balancing supply and demand, fraud detection, etc.

Since interview patterns shift with the latest industry trends and requirements, take advantage of our updated list of popular questions recently asked in DoorDash data science interviews.

1. How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?

Data science projects often have tight deadlines in fast-paced environments like DoorDash. This question evaluates your ability to manage multiple tasks effectively.

How to Answer

Start by discussing how you assess each task’s urgency and importance. Then, outline your method for organizing tasks, such as using tools.

Example

“To prioritize multiple deadlines, I first evaluate the urgency and impact of each task based on project requirements, stakeholder needs, and potential impact. I use a combination of tools like Trello and the Eisenhower Matrix to organize tasks based on their importance and deadlines. This ensures that I focus on high-impact tasks while meeting deadlines effectively.”

2. Tell me about a time when you exceeded expectations during a project. What did you do, and how did you accomplish it?

DoorDash values employees who go above and beyond. This question aims to gauge your ability to deliver exceptional results and your approach to achieving them.

How to Answer

Describe a project where you not only met but exceeded expectations. Discuss its challenges and your efforts to address them.

Example

“In a previous role, I was tasked with optimizing the pricing strategy for a ride-sharing app. While the initial goal was to increase revenue by 10%, I identified an opportunity to use dynamic pricing algorithms based on real-time demand and supply data. By implementing this innovative approach, we not only surpassed the revenue target by 15% but also improved customer satisfaction scores by 20% due to more transparent and fair pricing. My ability to think outside the box and implement cutting-edge solutions played a crucial role in exceeding expectations for this project.”

3. What makes you a good fit for DoorDash?

This question evaluates your understanding of the company’s mission and culture and how you align with it.

How to Answer

Emphasize your passion for DoorDash’s mission and values. Highlight aspects of the company that resonate with you. Also, mention how your skills and experience make you a valuable addition to the team.

Example

“I’m excited about the opportunity to join DoorDash because of its commitment to revolutionizing the food delivery industry and providing convenient, reliable service to customers. I’m particularly drawn to the company’s focus on leveraging data science to optimize operations and improve the delivery experience. With my background in machine learning and data analysis, I’m confident that I can contribute to DoorDash’s success.”

4. Data science rarely operates in a silo. Describe a situation where you had to collaborate with stakeholders to translate your findings into actionable insights.

Data scientists at DoorDash frequently need to collaborate with teams of software engineers, data analysts, and marketers. This question assesses your ability to work with stakeholders to translate data insights into actionable strategies.

How to Answer

Describe a project where you collaborated with stakeholders to analyze data and develop actionable insights. Highlight your communication skills, ability to understand stakeholder needs, and your role in driving decision-making based on data.

Example

“During a project to improve customer retention, I collaborated closely with the product and marketing teams to analyze customer behavior data. By conducting a comprehensive segmentation analysis, we identified key customer personas and their pain points. I facilitated workshops where we translated these insights into targeted marketing campaigns and product feature enhancements. This collaborative effort resulted in a 25% increase in customer retention rates within six months, demonstrating the effectiveness of data-driven decision-making and cross-functional collaboration.”

5. The data science landscape is constantly evolving. Tell me about a time you had to learn a new tool or technique to tackle a specific data challenge. How did you approach the learning process, and how did your method benefit your project?

Continuous learning is essential in the evolving field of data science. The DoorDash data science interviewer will evaluate your adaptability and willingness to learn new tools or techniques to tackle challenges.

How to Answer

Describe a specific instance where you had to learn a new tool or technique to address a data challenge. Discuss your approach to learning and how you applied the new knowledge to benefit the project.

Example

“When confronted with a data anomaly detection task, I encountered a scenario where traditional statistical methods were insufficient due to the complexity and volume of the data. Recognizing the need for a more advanced approach, I delved into research papers and online tutorials to learn about deep learning techniques for anomaly detection. After gaining a solid understanding, I implemented a convolutional autoencoder model and fine-tuned it to detect anomalies in real-time streaming data. This approach not only addressed the specific challenge at hand but also enhanced our overall anomaly detection capabilities, demonstrating the value of continuous learning in driving innovation.”

6. Let’s say we want to build a new delivery time estimate model for consumers ordering food delivery. How would you determine if the new model predicts delivery times better than the old model?

The data scientist position interviewer at DoorDash may ask this question to understand your approach to improving their delivery time estimation model, a critical component of their service.

How to Answer

You can propose methods such as cross-validation, splitting data into training and testing sets, and comparing metrics like mean absolute error or root mean squared error between the old and new models.

Example

“To evaluate the new delivery time estimate model, I would first split the data into training and testing sets. Then, I would use metrics like mean absolute error or root mean squared error to compare the new model’s performance against the old one on the testing set. Additionally, I might consider conducting cross-validation to ensure the robustness of the evaluation.”

7. Let’s say we have a payment structure for delivery drivers where they make 5% of every order. A product manager wants to launch a new payment structure for delivery drivers where each makes 2.5% of each order and $50 after each fifth order. How would you determine the success of this new structure?

Data scientists at DoorDash are expected to understand the real-world business implications of decisions. This question evaluates your ability to analyze the impact of changes to a payment structure on business outcomes.

How to Answer

You could suggest analyzing metrics such as driver satisfaction, delivery times, and overall profitability to assess the new payment structure’s success.

Example

“To determine the success of the new payment structure for delivery drivers, I would analyze several key metrics. First, I would look at driver retention rates to see if the new structure affects driver satisfaction and loyalty. Next, I would examine average order delivery times to ensure that changes in driver compensation do not negatively impact service quality. Finally, I would assess overall profitability, considering both the impact on delivery costs and customer satisfaction. By monitoring these metrics before and after implementing the new payment structure, we can gain insights into its effectiveness in balancing driver incentives with company objectives.”

8. You work as a data scientist for a grocery store chain that has a mobile app. At the end of the checkout process on the app, users are presented with an up-sell carousel, a sliding display of items that users can scroll/swipe to view and add to their cart. Currently, the carousel only presents items from the store’s personal brand. How would you determine whether the carousel should replace store-brand items with national-brand products of the same type?

User experience optimization and increasing conversion rates are among DoorDash’s key strategies. This question in your DoorDash data scientist interview will demonstrate your ability to make data-driven decisions regarding product presentation.

How to Answer

You can propose methods such as A/B testing or analyzing user engagement metrics to compare the effectiveness of the carousel with store-brand items versus national-brand products.

Example

“To evaluate whether the carousel should replace store-brand items with national-brand products, I would conduct A/B testing. By randomly presenting users with either version of the carousel and analyzing metrics like click-through rates and conversion rates, we can determine which approach leads to higher user engagement and sales.”

9. What are the logistic and softmax functions? What is the difference between the two? What makes them useful in logistic regression?

This question evaluates your understanding of activation functions commonly used in logistic regression and neural networks.

How to Answer

Explain the mathematical formulas and characteristics of both logistic and softmax functions, emphasizing their suitability for different tasks.

Example

“The logistic function, also known as the sigmoid function, maps input values to a range between 0 and 1, making it suitable for binary classification problems in logistic regression. In contrast, the softmax function extends the logistic function to handle multiple classes by normalizing the output as probabilities across all classes. This makes softmax ideal for multi-class classification tasks like those encountered in logistic regression.”

10. Suppose you have an events table that tracks user activities on a website. Write a query to identify and label each event with a session number. All events in the same session should be labeled with the same session number.

Note: A session consists of a series of consecutive user events within 60 minutes of each other.

For example, if a user has a series of events at 00:01:00, 00:30:00, and 01:01:00, this would be considered 1 session, but a series of events at 00:01:00, 00:30:00, and 01:31:00 would be 2 sessions.

Example:

Input:

events table

Column	Type
id	INTEGER
created_at	DATETIME
user_id	INTEGER
event	VARCHAR

Output:

Column	Type
created_at	DATETIME
user_id	INTEGER
event	VARCHAR
session_id	INTEGER

DoorDash may ask this question to understand your ability to work with event data, which is relevant for analyzing user behavior on their platform.

How to Answer

You can propose an SQL query that utilizes window functions and a conditional aggregation to assign session numbers to consecutive events within 60 minutes for each user.

Example

WITH session_starts AS (
	SELECT created_at,
	       user_id,
	       event,
         CASE
	 WHEN TIMESTAMPDIFF(MINUTE, LEAD(created_at) OVER(PARTITION BY user_id ORDER BY created_at DESC), created_at) > 60 OR TIMESTAMPDIFF(MINUTE, LEAD(created_at) OVER(PARTITION BY user_id ORDER BY created_at DESC), created_at) IS NULL THEN 1
		       ELSE 0
        END AS is_new_sesh
	FROM events
	ORDER BY user_id, created_at DESC
)

11. Let’s say we have a table representing a company payroll schema. Due to an ETL error, the employees table did an insert instead of updating the salaries every year when doing compensation adjustments. The head of HR still needs the current salary of each employee. Write a query to get the current salary for each employee.

Note: Assume no duplicate combination of first and last names (i.e., no two John Smiths). Assume the INSERT operation works with ID autoincrement.

Example:

Input:

employees table

Column	Type
id	VARCHAR
first_name	VARCHAR
last_name	VARCHAR
salary	INTEGER
department_id	INTEGER

Output:

Column	Types
first_name	VARCHAR
last_name	VARCHAR
salary	INTEGER

Handling ETL errors is a typical task for DoorDash data scientists. The interviewer may ask this question to assess your SQL and problem-solving skills when dealing with data integrity issues.

How to Answer

You need to write an SQL query that selects the current salary for each employee from the employees table. Since there are duplicates, you need to consider the latest entry for each employee.

Example

SELECT e.first_name, e.last_name, e.salary
FROM employees AS e
INNER JOIN (
    SELECT first_name, last_name, MAX(id) AS max_id
    FROM employees
    GROUP BY 1,2
) AS m
    ON e.id = m.max_id

12. You are generating a yearly report for your company’s revenue sources. Calculate the percentage of all total revenue made to date during the first and last years recorded in the table. Round the percentages to two decimal places.

Example:

Input:

annual_payments table

Columns	Type
amount	INTEGER
created_at	DATETIME
status	VARCHAR
user_id	INTEGER
amount_refunded	INTEGER
product	VARCHAR
id	INTEGER

Output:

Columns	Type
percent_first	FLOAT
percent_last	FLOAT

This question assesses your SQL skills in calculating percentages and working with date-related data.

How to Answer

Write an SQL query to calculate the percentage of total revenue made during the first and last years recorded in the table.

Example

WITH cte AS ((
   SELECT
      created_at,
      SUM(amount - amount_refunded) OVER (PARTITION BY YEAR(created_at)) percents,
      ROW_NUMBER() OVER (ORDER BY YEAR(created_at)
         DESC)
      LAST,
      ROW_NUMBER() OVER (ORDER BY YEAR(created_at))
      FIRST
   FROM
      annual_payments)
),
cte2 AS (
   SELECT
      SUM(amount - amount_refunded) s
   FROM
      annual_payments
)
SELECT
   ROUND((
   SELECT
      percents FROM cte
   WHERE
      FIRST = 1) * 100 / (
      SELECT
         s FROM cte2), 2) percent_first, ROUND((
      SELECT
         percents FROM cte
      WHERE
         LAST = 1) * 100 / (
         SELECT
            s FROM cte2), 2) percent_last

13. Delivery times can fluctuate based on factors like traffic, weather, and restaurant distance. How would you design a machine learning model to predict delivery times for upcoming orders?

As a DoorDash data scientist, you must be aware of the variables associated with your project and develop machine-learning models based on those. This question checks your ability to design a machine-learning model to solve a real-world problem related to delivery times.

How to Answer

Describe the steps to design a machine learning model for predicting delivery times, including data collection, feature selection, model training, and evaluation.

Example

“To design a machine learning model for predicting delivery times, I would start by collecting historical data on orders, including factors such as order size, distance to the restaurant, traffic conditions, weather, and time of day. Then, I would preprocess the data, selecting relevant features and handling missing values or outliers. Next, I would choose an appropriate machine learning algorithm, such as regression or gradient boosting, and train the model using the prepared data. Finally, I would evaluate the model’s performance using metrics like mean absolute error or root mean squared error and fine-tune it as needed.”

14. How would you calculate customer lifetime value (CLTV) for DoorDash users? Explain the factors you would consider and how this metric can inform customer retention strategies.

DoorDash may ask this question to gauge your ability to quantify the value of customers over their lifetime and formulate strategies for retaining them.

How to Answer

Explain the factors involved in calculating CLTV, such as customer acquisition cost, purchase frequency, average order value, and customer retention rate. Additionally, discuss how CLTV can inform customer retention strategies, such as targeted marketing campaigns or loyalty programs.

Example

“To calculate customer lifetime value (CLTV) for DoorDash users, I would consider factors such as the average order value, order frequency, customer acquisition cost, and customer retention rate. By analyzing these metrics over a certain period, such as a year, I can estimate the expected revenue generated by each customer during their lifetime with DoorDash. CLTV can inform customer retention strategies by identifying high-value customers who may warrant special incentives or personalized offers to encourage repeat orders and enhance their lifetime value to the company.”

15. DoorDash wants to optimize its dasher assignment process to ensure timely deliveries. Describe a machine learning approach to matching dashers with orders.

This question assesses your ability to apply machine learning techniques to optimize business processes, specifically the dasher assignment process for timely deliveries.

How to Answer

Describe a machine learning approach to matching dashers with orders, including data collection, feature engineering, model selection, and deployment.

Example

“To optimize dasher assignment at DoorDash, I would use a machine learning approach that considers various factors such as dasher location, order location, estimated delivery time, historical delivery performance, and current workload. I would collect data on past orders, including order details and dasher assignments, and engineer features such as distance between dasher and restaurant, estimated travel time, and dasher availability. Then, I would train a machine learning model, such as a decision tree or neural network, to predict the optimal dasher for each order based on these features. Finally, I would deploy the model into production to automatically assign dashers to incoming orders in real-time, optimizing for timely deliveries and customer satisfaction.”

16. NPS is a metric that gauges customer satisfaction. How would you use NPS data to improve the DoorDash user experience?

Customer satisfaction is pretty important to DoorDash, a primarily B2C business. The interviewer may ask this question to evaluate your ability as a data scientist to use NPC data to improve the user experience.

How to Answer

Use NPS data to identify areas for improvement in the user experience, prioritize enhancements based on feedback, and track changes over time to gauge effectiveness.

Example

“I would segment NPS feedback by key touchpoints in the user journey, such as order placement, delivery time, and customer support interactions. Then, I’d prioritize improvements based on recurring themes and sentiments, aiming to address pain points identified by users. Tracking NPS scores over time would help assess the impact of these changes on overall satisfaction.”

17. DoorDash wants to predict order volume for specific times and locations. This helps optimize staffing and restaurant partnerships. Explain how you would approach demand forecasting using machine learning.

DoorDash may ask this question to gauge your ability to apply machine learning techniques to real-world business challenges.

How to Answer

Collect historical order data, including time, location, and other relevant factors, then apply machine learning techniques such as time series forecasting or regression to predict future order volumes.

Example

“I’d gather historical data on orders, considering factors like time of day, day of week, location, and promotional events. Then, I’d apply time series forecasting models like ARIMA or machine learning algorithms such as gradient boosting or LSTM networks to predict future order volumes. Regular model evaluation and refinement would ensure accuracy, aiding in staffing and restaurant partnership optimization.”

18. Describe your approach to A/B testing different user interface elements and marketing messages to optimize DoorDash conversion rates.

With this question, you can demonstrate your methods for conducting A/B tests to enhance conversion rates, a crucial aspect of optimizing user experience and marketing effectiveness.

How to Answer

Design controlled experiments in which users are randomly assigned to different versions of UI elements or marketing messages. Then, collect relevant metrics such as click-through rates or conversion rates and analyze the results to determine the most effective variations.

Example

“I’d start by defining clear hypotheses for each A/B test, specifying the UI elements or marketing messages to be tested and the expected impact on conversion rates. Then, I’d randomly assign users to different versions of these elements, ensuring statistical significance. After collecting data on relevant metrics, such as click-through rates or conversion rates, I’d use statistical analysis to compare the performance of different variations and determine which ones lead to the highest conversion rates.”

19. DoorDash occasionally receives fraudulent orders. Write an SQL query to identify suspicious order patterns. This could involve analyzing orders with high values placed from new accounts, orders with unusual delivery addresses, or a surge of orders from the same location.

This question evaluates your SQL skills and ability to detect suspicious order patterns, which is essential for fraud prevention.

How to Answer

Craft an SQL query that selects orders meeting specific criteria indicative of fraudulent activity, such as unusually high values, new account creation, suspicious delivery addresses, or clustering of orders from the same location.

Example

SELECT *
FROM orders
WHERE order_value > 1000
	OR new_account = 1
	OR delivery_address IN (SELECT address FROM suspicious_addresses)
	OR location IN (SELECT location FROM orders GROUP BY location HAVING COUNT(*) > 5);

20. How would you identify and address outliers in DoorDash’s delivery time data? Discuss different methods for outlier detection and the potential impact of outliers on different analyses.

Outlier detection techniques and their impact on data quality are critical to working as a data scientist at DoorDash. This question evaluates your understanding of outlier detection techniques and their impact on data analysis.

How to Answer

Detect outliers in delivery time data using statistical methods like z-score analysis or interquartile range (IQR). Then, consider strategies such as removing outliers, transforming data, or using robust statistical techniques.

Example

“I’d start by calculating z-scores or IQRs for delivery time data to identify outliers. For example, I could consider any delivery time falling more than 3 standard deviations from the mean as an outlier. Depending on the impact of outliers on analyses, I might choose to remove them, transform the data using methods like log transformation, or use robust statistical techniques that are less sensitive to outliers, such as median-based methods.”

As mentioned, SQL, ML concepts, and product metrics get prioritized in DoorDash data scientist interviews. Behavioral questions and your alignment with the work culture and values are also significant factors for DoorDash. Here are a few tips to excel in the interview:

Understand DoorDash and Its Business Model

DoorDash emphasizes real-world problems in the interview, which often relate to recent industry trends and technological advances. Follow DoorDash on its website, LinkedIn, X, and other social media platforms to stay updated on corresponding news and happenings.

Review Core Data Science Concepts

Despite having an affinity for SQL and ML questions, your DoorDash interviewer might explore core data science concepts and statistics questions. To better prepare, review the concepts from our Learning Paths and answer the top data science interview questions.

Practice Coding Questions

At the DoorDash data scientist interview, you’ll be asked to demonstrate your coding prowess, and data science SQL questions will take priority. During the live coding rounds, you may also be given a real-world decision-making problem employing machine learning modeling and a database issue with SQL queries.

Master Case Studies and Takehomes

Given the short time frame, you’re expected to complete the take-home assignment quickly. To ace it, prepare with our curated data science take-home challenges.

Of course, case studies are often the most challenging aspect of data scientist interviews at DoorDash. The interviewer may tailor the question to resemble an existing or past project, evaluating your ability to convey insights and navigate obstacles.

Practice data science case study questions until you’re prepared to generate excellent answers during the interview.

Prepare Behavioral Questions

Behavioral questions enable your interviewer to understand your interest in the role and experience with recent related projects. Prepare with our list of data science behavioral questions to learn how to tackle tricky questions and answer them according to your DoorDash interviewer’s preferences.

Attend Mock Interviews and Get Professional Feedback

Mock interviews with a proper feedback loop can help hone your approach toward the interview and fine-tune your answers to behavioral and technical questions. Our P2P Mock Interview portal, complete with the AI-assisted Interview Mentor, can produce a noticeable difference in confidence between you and other candidates.

How Much Do Data Scientists at DoorDash Earn in a Year?

On average, data scientists at DoorDash earn around $170,000 in base pay and $222,000 in total compensation. The more senior the role, the higher the earnings.

Visit our website for insight into the industry’s data scientist salary structure.

What Companies Can I Work at as a Data Scientist Other than DoorDash?

Data scientists are needed in almost every company like DoorDash, including Uber, Grubhub, and Instacart, where you can expect to be valued and compensated fairly. Follow our main company interview guide to explore other companies and positions.

Are there Job Postings for DoorDash Data Science Roles on Interview Query?

Yes, we have the latest openings listed for the DoorDash data scientist role. Please visit our job board to stay updated on open positions.

DoorDash encourages innovation by believing in providing the right tools, resources, and opportunities for everyone. Your opportunity is the interview for the data scientist role at DoorDash, where your behavioral and technical skills will be thoroughly evaluated against hundreds of other candidates.

Gaining an edge against all the odds requires understanding the fundamentals of data science, statistics, SQL queries, and machine learning models. Your communication skills will also significantly contribute to your overall success in the data scientist interview.

Still considering your options? Explore our other interview guides for the business analyst, data analyst, data engineer, and product analyst at DoorDash. Also, don’t forget to visit our main DoorDash interview guide for more clarity into the process and different roles.