DoorDash is making its mark as the world’s most reliable on-demand logistics engine for delivery. As a result of their growth, they need to grow their data science team to help scale their business. More data scientists help develop and improve the models that power DoorDash’s three-tier marketplace of consumers, merchants, and dashers.
DoorDash is aware of the importance of data and the need for a high-energy, confident, and well-experienced data scientist. An existing background in logistics also doesn’t hurt, especially when you have Amazon, Uber, or Lyft on your resume. Additionally after that, any company with marketplace effects.
The general requirements are below:
What are the skills required? DoorDash hires only qualified and experienced candidates with 2+ years of industry experience (4+ years for senior data scientist role) in designing and developing machine learning models with an eye for business impact.
General qualifications include:
At DoorDash, they have 3 different teams related to data science.
Analytics Data Scientist: This team focus on experimental analysis with emphasis on building dashboards and doing the analysis that supports specific business goals.
Machine Learning Engineers: This team focus on building the bulk of the infrastructure for deploying models.
Data Science Machine Learning: This team sits right in the middle of the former two. They build models that focus on business impact. Their focus is on experimental analysis, building recommendation systems and features, building pipelines for recommendations, designing marketing attribution and segmentation, and building sales models.
Although these three teams are separate and work independently, in some cases, they work very cross-collaboratively.
Check out our guide Machine Learning Interview Questions for example questions and tips.
The DoorDash Data Scientist application process is not different from the application processes of most tech companies. The process starts with:
(1) An initial phone screen by a recruiter.
(2) You receive a take-home challenge where you will be graded on your ability to build a machine learning model.
(3) The next step is the take-home challenge review call if you pass the assignment. A data scientist will ask a few questions on how you crafted the solution and go through your thought process.
(4) The last stage is the onsite interview where you will be tested on machine learning, coding, business, and mission values.
After applying for the job, you will get a phone interview with a recruiter. This initial phone call interview by a recruiter usually lasts for 30 minutes. You will be asked a few questions about projects and background related to data science.
Want a preview of the DoorDash take-home challenge? Need a take-home challenge review? We have both at Interview Query.
At this stage, you will be given a take-home problem/dataset via email. This take-home challenge usually takes a few days to complete (48 to 72 hours in most cases) and is crafted depending on which data science role you’ve applied to.
The analytics take-home challenge is divided into two segments. This first part involves analysis on data set (using a case study data provide). The second segment will require you to write SQL queries and answer a few SQL questions.
The data science machine learning take-home challenge is also two parts. The first part requires building a model to** predict delivery duration** while the second part is to create an application that can serve the model from part 1.
Example Questions:
After submitting the take-home problem, depending on if you pass the challenge, you’ll receive a review call (video chat) with a data scientist on the case study given. At this point, you will be asked a series of questions about the techniques used. The interviewer is just trying to get a grasp of your thought process and understand why you made certain decisions.
The on-site interview lasts for about 5 hours with a lunch break in between. You will be introduced to the data scientist team along with other team members that work closely together.
Here’s what the on-site interview looks like:
During the on-site interview, you may be given a real-life DoorDash problem to work on and present to the interview panel as various team members pair the program with you. Depending on the type of data science role, expect it to be heavy on either analytics or building a machine learning model. It’s important when presenting at the end to focus on how machine learning affects business problems.
Try answering this interview question asked by DoorDash on Interview Query.
Say you’re running an e-commerce website. You want to get rid of duplicate products that may be listed under different sellers, names, etc… in a very large database.
For example, iPhone X and Apple iPhone 10
How do you go about doing this?
Here are some questions asked previously at the DoorDash Data Science interview.
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