7 Technical & Behavioral Data Scientist Interview Questions to Practice

7 Technical & Behavioral Data Scientist Interview Questions to Practice

Data Scientists are a hot commodity. The U.S. Bureau of Labor Statistics projects that demand for Data Scientists will grow 35% by 2035, which is much faster than other occupations. Despite the wealth of open positions in the field, you’ll still need to ace your interviews to land one.

To help you prepare, we’ll walk you through what you can expect during a Data Scientist interview, some common questions, and tips for answering them. Challenge yourself to respond to open-ended questions in our revamped Data Analyst and Data Scientist Interview Prep programs. Receive instant feedback from AI based on your answers, highlighting any areas that could use improvement. Or, for a more in-depth exploration of a Data Scientist’s fundamental knowledge and skills, check out our Data Scientist: Machine Learning Specialist career path.

What should someone expect when going in to interview for a job as a Data Scientist?

When going in for a Data Scientist interview, you can expect your interviewer to ask questions that test your knowledge of gathering, analyzing, and applying data.

Also, because Data Scientists need to produce, examine, and interpret datasets, the interviewer will want to see your problem-solving skills. They might also check on your ability to work with others and maintain a focus on high-level objectives — not just the figures on your screen.

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You can also expect them to ask questions about your work history, challenges you’ve faced, and why you want to work at their company. Even though Data Scientists spend a lot of their time working with data, working with people is just as important — so you should expect some questions about your soft skills along with your technical knowledge.

Data Scientist interview questions

Data Scientist interview questions can be grouped into two basic categories: technical or behavioral. Technical questions help the interviewer gauge your familiarity with the tools you’ll need as a Data Scientist. Behavioral questions help illustrate your personal competencies and thought processes. While the technical questions may seem more difficult on the surface, you should take the time to prep for both.

Behavioral Data Scientist interview questions

Again, behavioral interview questions help your interviewer understand who you are as a person. These questions often center around your experience, thought processes, aspirations, and motivations. Here are a few common behavioral Data Scientist interview questions.

Why do you want to be a Data Scientist at this company?

This question gives you the opportunity to talk about:

  • Why you enjoy being a Data Scientist
  • Specific aspects of the company’s products or services that draw you to it
  • Elements of the company culture that you find attractive
  • Specific projects the company has been involved with that appeal to you

When answering this question, you’ll want to show that you’ve done your research about the company and what it offers. You should also explain why you’re passionate about data science as a problem-solving tool.

How do you deal with challenges?

To answer this question, you’ll want to show you know:

  • How to solve problems involving both data and interpersonal relationships within a team setting
  • How to stay calm under pressure
  • How to communicate with others on your team when addressing an issue
  • How to both lead and follow when a challenge arises

Discussing how you address challenges that arise within a team environment allows you to showcase your ability to listen, communicate effectively, and help yourself and others focus on the team’s objective. While talking about challenges related to gathering, organizing, and analyzing data, you have the chance to show your ability to think creatively and find solutions.

How has your previous experience prepared you for this role?

Data Scientist interview questions like this help your interviewer assess your understanding of the role for which you’re applying, as well as your ability to use your experiences as learning tools. Explain how universal skills like effective communication and collaboration transfer to your new role. You could also highlight any relevant tools or techniques you’ve used in previous positions.

Technical Data Scientist interview questions

Technical interview questions often focus on how you approach the challenges Data Scientists face every day. In some cases, the question itself may not specifically allude to any technical tools, but you should still view your answer as an opportunity to show you know which tools and techniques best solve key problems.

Before the interview, it’s a good idea to mentally list the tools you’ve used to solve problems, ranging from data science-specific tools like SQL and Python to general tools like Excel and PowerPoint. You may also want to take a few data science courses to brush up on any skills that are listed as preferred or required on the job description.

What is selection bias, and why is it important to avoid it?

Selection bias refers to data samples that aren’t randomly selected. Avoiding selection bias is important because insights derived from biased datasets aren’t useful. While answering this question, discuss the tools and methods you’ve used to avoid selection bias, such as weighting, boosting, and resampling data.

Are large datasets always the best choice?

The best way to answer a question about the ideal size of a dataset is to explain how it depends on the context of the situation. Then, provide examples of how different circumstances require different sizes.

For example, large datasets aren’t always cost-effective because they require vast amounts of computational power, human resources, and time to maintain. Plus, there might even be redundancies in your data. In many situations, you can use a smaller dataset without sacrificing the validity of your results.

What is meant by root cause analysis?

Root causes analysis involves reverse-analyzing an issue to figure out the original problem that caused it. But, your answer to this question shouldn’t stop after you’ve provided a definition. Provide an example of how you used root cause analysis to solve a problem either at a previous job or in your studies.

How do you find outliers in a set of data?

While answering this question, showcase your understanding of outliers by explaining how they jeopardize the integrity of your dataset and can possibly lead to errors in your results. Then, explain how you identify outliers with methods and techniques like composing histograms or using quartiles or standard deviations.

By practicing the technical and behavioral questions above, you’ll be well on your way to landing a position as a Data Scientist. Remember to prepare ahead of time so you’re comfortable and ready to show why you’re the best choice for the job. In our upgraded Data Analyst and Data Scientist Interview Prep paths, you can assess your responses to open-ended questions, receive immediate AI-generated feedback, and pinpoint areas for improvement.

If you’re still feeling uncertain and want to ensure you’re well-prepared, check out our Data Scientist: Machine Learning Specialist career path. You’ll learn all you need to know to gain an entry-level position in the field. Plus, you’ll also build a portfolio that showcases your skills to future employers.

This blog was originally published in July 2021 and has been updated to include details about our new AI interview prep features.