Getting ready for data science interview questions is, in some respects, no different than preparing for an interview in any other industry. You’ll research the company, prepare answers to common interview questions, and review your portfolio to use during the interview.
However, preparing for a data science interview involves more than preparing for questions like “Why do you think you are qualified for this position?” Data scientist interviews include a lot of technical topics. And while you might be comfortable talking about your abilities, can you explain them in a way that makes sense to the hiring manager?
Preparing for a Data Scientist Interview
It’s not uncommon for a data scientist applicant to go through three to five interviews for the role. This can include a phone interview, Zoom interview, in-person interview, and panel interview.
Crush the coding test
As you might expect, many of the interview questions will focus on your hard skills. However, you can also expect questions about your soft skills, as well as behavioral interview questions that assess both your hard and soft skills.
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Here’s how you can prepare for your data scientist interview.
Go Back to Basics
Start by brushing up on the fundamentals of data science. Review:
- Statistical analysis: collecting and analyzing large datasets to identify and uncover trends or cause and effect
- Data hygiene: cleaning and formatting raw data to ensure it’s accurate
- Coding: writing instructions in “computer speak.”
- Programming: creating the software or system that executes the coding
- Data modeling and visualization: presenting data visually to help establish the relationship between data points
Jenna Bellassai, lead data reporter at Forage and former data scientist at Guru, advises applicants to “review fundamental programming and machine learning concepts. Be prepared to describe your contributions to previous projects.”
While part of your interview prep likely involves researching the company, Bellassai says that to prepare for data science interview questions, you should also “think about what the company’s data may look like, what technical challenges they may face, and where machine learning models could play a role in their business. If you have experience with a niche technology or modeling approach that the company uses, be prepared to speak about it.”
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Review Possible Interview Topics
Most interviews include questions about the specifics of the role, and a data scientist interview is no different. Bellassai says you can expect technical questions on these topics:
- Software engineering
- Data manipulation
- Statistical modeling (including machine learning topics)
- Architecture design
- Distributed computing
- Cloud architectures
- Working with specific types of data, like geospatial data
Bellassai also notes that during the interview, you may have to solve a coding problem or draw an architecture diagram.
10 Most Common Data Science Interview Questions
While there are no guarantees, here are 10 interview questions you’ll likely encounter:
- What is the difference between supervised and unsupervised learning?
- What is the difference between data science and data analytics?
- Explain the steps in making a decision tree. How would you create a decision tree?
- You’re given a data set that’s missing more than 30% of the values. How do you deal with that?
- How do you/should you maintain a deployed model?
- How is data science different from other forms of programming?
- How often do you/should you update algorithms?
- What is the goal of A/B testing?
- What are the differences between overfitting and underfitting, and how do you combat them?
- What do you prefer using for text analysis?
Bellassai also notes that while you should give your best answer to the question, there is no “right” answer. “Remember that there is no perfect solution. A particular approach isn’t necessarily the best just because you’ve used it before.”
Bonus Round: 10 Common Behavioral Interview Questions for Data Scientists
Technical skills aren’t the only kind of data science interview questions you’ll encounter. Like any interview, you’ll likely be asked behavioral questions. These questions help the hiring manager understand how you’ll use your skills on the job.
While your answers will be specific to the role, use the STAR Method to tell a story about a time you put your skills to work and what the outcome was.
Here are 10 behavioral questions you might encounter in a data scientist interview:
- Tell me about a time you used data to bring about change at a job.
- Have you ever had to explain the technical details of a project to a nontechnical person? How did you do it?
- What are your hobbies and interests outside of data science?
- Tell me about a time when you worked on a long-term data project. How did you approach collecting and analyzing data when different parts of the project had different deadlines?
- How do you align data projects with company goals?
- Tell me about a project that didn’t go as planned. How did you manage and overcome the obstacles you encountered?
- Do you contribute to open-source projects?
- Walk me through a project you’re currently (or recently) working on.
- Looking back on a project, what would you do differently to improve it?
- How do you go about deciding what should and should not be measured? What happens if members of the team disagree with your opinions?
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