Interview Guide Jun 20
Jun 203 rounds
The role of a Data Scientist at DoorDash is to use data to drive decision making and strategy. They collect, analyze, and interpret large sets of data to identify trends and insights that can inform the company's operations and growth.
Data Scientists are embedded across the company in critical roles. For instance - key responsibilities of a Data Scientist working in the Pricing domain at DoorDash include developing and then implementing statistical models which can predict customer behavior and inform pricing strategies. Other Data Scientists working on the Dasher side are responsible for using data to optimize delivery routes and improve the overall efficiency of the company's logistics.
In addition to analyzing data, a Data Scientist at DoorDash also plays a key role in the development and maintenance of the company's data infrastructure. They work closely with the engineering team to ensure that data is properly collected, stored, and protected.
Data Scientists at DoorDash also have to collaborate with cross-functional teams, including product, marketing, and operations, to inform and influence company strategy.
DoorDash hires Data Scientists across the company and there are different seniority levels depending on the scope and expected impact. They have Senior level roles and some openings for Data Science Managers along with some Data Analyst and ML Engineer roles.
Note that the availability of these positions is subject to change and may not be available at all times, you could check DoorDash's career page and linkedin page for the latest and more detailed information.
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 company's 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. If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters will give you feedback on your resume.
Responsibilities of a Data Scientist at DoorDash
The responsibilities of a data scientist at DoorDash across roles can broadly be seen as-
- Design and build data infrastructure that supports DoorDash's data science efforts, including data collection, storage, and protection.
- Identify key business metrics and develop reporting and visualization tools to track them.
- Develop and build models across a range of 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.
- Continuously monitor and analyze performance of models, and iterate as necessary.
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.
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.
As a part of the DoorDash Data Scientist interview, the candidate will need to go through multiple interview rounds:
1. HR interview - The first round is an HR interview. This session takes place so that the HR team can better understand your background and can help you understand the role.
2. The second round is a coding round in which you have to solve coding questions related to SQL. This round consists of SQL questions with medium complexity. DoorDash interviewers will often be totally fine with you searching the internet to help guide your answers since this is how work is done in real life. 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. The third round consists of business intuition interviews. 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. During the third round, the interviewer will evaluate the candidate's ability to comprehend the given case and meet DoorDash's expectations by asking follow-up questions.
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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.
During the HR interview, the focus is typically on assessing if your abilities align with the position being applied for. This may include informal queries about your experiences and qualifications. The goal of this session is to provide the HR team 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.
- 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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The second round is a coding round in which you have to solve coding questions related to SQL. This round consists of SQL questions with medium complexity with internet access available. In some cases, this round can also consist of a rapid-fire of SQL questions. Some candidates reported facing Senior Data Scientists and the key was to be quick and accurate with your solutions.
- 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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The third round consists of business intuition interviews. These interviews usually comprise a live business case study that is usually about a new product or a service for DoorDash’s business 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. During the third round, the interviewer will evaluate the candidate's ability to comprehend the given case and meet DoorDash's expectations by asking follow-up questions. Some candidates reported facing a technical screening along with the business case study similar to the previous round.
Please note that this round is role specific and you might encounter a different case depending on the role you are applying for.
- 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?
When you are preparing for a DoorDash Data Science interview - we’d recommend keeping the following in mind:
- Make sure you practice and work on advanced SQL techniques. The topics you should especially focus on are - GroupBy, Aggregation, Window Functions and Date Time Based SQL Questions.
- When applying for a DS Manager, leadership qualities are very essential and will be highly valued. Hence, you should emphasize on any DS projects you led in the past.
- Read about the business fundamentals behind DoorDash and how they prioritize customer satisfaction. For instance - understand the multi-sided business model of DoorDash. Nearly every Prepfully candidate reported that it came up in their case study - so it's very useful to have pre-thought the dynamics involved, the metrics you'd track and how you'd test hypotheses within this ecosystem. You need to constantly keep the interplays of restaurants, bookers and dashers in mind at all times - since most experiments will meaningfully affect metrics beyond their direct scope.
- Be clear with your technical knowledge required to fit into the specific role you’re interviewing for at DoorDash (most roles require you to index more heavily on specific aspects of the craft).
The interview process for a Data Scientist role at DoorDash typically includes 3 primary rounds - a HR interview, a coding round, and the final business case study. During the HR interview, 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.