5 Questions to Expect in Your Data Science Job Interview

Rather than list incredibly specific questions that you can quickly memorize, I wanted to highlight some more conceptual questions that may seem trivial at first but that can end up being a key to landing a data science job. You’ll notice that these questions aren’t purely technical, but rather show how you approach your work as a data scientist. Everyone can study SQL, Python, R and so on, but what sets you apart is how you work with data science projects and problems and the people who are also dealing with those same projects and problems alongside you. Here, I will be discussing some more behavioral data science interview questions. Perhaps you will encounter some of these questions and tasks in the future or ask them yourself as the interviewer.

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Why Are You Interested in Data Science?

This question seems like one you could possibly gloss over when preparing for your interview. Don’t do that, though. The interviewer will definitely want to know why you’re interested in data science because this information will show them that you have the passion and motivation to be successful even when the job gets difficult or overwhelming.

The tricky part is answering that question since showing and not just telling is the best approach. Bear in mind also that ,although the interviewer might not ask the question directly, your actions will speak for you. For example, the way that you communicate before the interview even starts and after it ends can show your interest in data science. 

What not to do:

• Immediately ask about pay. Who knows, you might get paid more after they see your interest in the actual job because that will show that you love data science as well as the company, which benefits them in the long run!
• Talk about data science as this new, hip job.

What you should do:

• Show that you go out of your way to learn and grow when it comes to data science. For example, earning certifications, writing and videos about the subject and anything extracurricular that you do shows them that you are immersed in the world of data science. Remember that you’re completely new to them so they wpn’t know everything you do. Judiciously parceling this information out throughout the interview process will help you land the job.
• Depending on your personal feelings, mention something about the positive effects data science can have on yourself, the company and others involved in its practice.

Overall, as simple and cliche as it sounds, be yourself, and the right job will come to you. Don’t pretend to be something you’re not for a specific company. Instead, focus on showing them what you truly enjoy about data science.

What Problems Have You Encountered?

This question exists to test your awareness of a project. Although only discussing a perfect work environment and projects might seem nice, you will most likely encounter some problems at your future job. With that being said, the interviewers will want to know what you did with past problems because that serves as an indicator of what you will do in the future.

What not to do:

• Put blame on others or the company you worked for.

What you should do:

• Be transparent about your mistakes. Everyone slips up sometimes, and that’s OK. Realizing this sooner rather than later is always better.
• Discuss common, recurring problems and bring up your solutions.
• Discuss what you learned from the problems you’ve faced.

Have You Worked With Stakeholders?

Of course, working with a stakeholder is beneficial as a data scientist. You might not have worked with someone specifically labeled with this title, however. So, keep in mind that anyone who shares a common interest can be considered a stakeholder. Traditionally, I would say that a a product manager is the most common type of stakeholder. Other data scientists, managers, business analysts, software engineers, UI/UX researchers, and pretty much anyone in your company or even your actual customers can be considered stakeholders as well.

The reason companies ask this question is because they expect you to be able to explain your work both to people who haven’t studied data science or those who aren’t on the product or engineering team (i.e., sales). You’ll need to explain how a model works or its benefits to these folks without complex, technical jargon. 

For example, you might not want to even use data science for a model, but instead to create a tool for automatically doing something that a human normally would. Depending on the situation, you might use more technical terms, but it is important to be aware of what others might not know. This is especially true of a one-off explanation. If you’re working with someone more than once, however, it might be beneficial to create some documentation that summarizes the project you’re working on for reference.

What Are Common Project Pitfalls and Considerations?

This question is a little more specific than just addressing problems. In this context, pitfalls means anticipating what could go wrong with a project. As a data scientist, you’ll need to know what to expect once the model is implemented. Expectations include, but are not limited to, things like resources, people involved, customer impact and monetary considerations.

Here are some common pitfalls to expect on a data science project that may be beneficial to bring up in your interview:

• How much data do you need?
• How often does your model need to be trained?
• How do you create a backup in case your training fails?
• How expensive is your model?
• Is your model taking up any other resources from other current processes?
• What impact do you expect your model to have on the business and customer?
• How do you handle your model performing poorly on certain data?
• What happens if you have a classification model that predicts incorrectly?

As you can see, a lot of questions, considerations and pitfalls accompany any data science project. You should be aware of these pitfalls and their solutions now instead of after the fact. One key phrase to remember is to be proactive, not reactive.

Perform a Take-Home Project

This point is less of a question than something to expect on some of your data science interviews. Data science is unique in that it involves a lot of cross-functional disciplines. One way to test this skill is to assign a take-home assessment, which is often purposefully vague. The point is to see what you would do in a situation without much information to solve a data science-related problem.

What to do:

• Ask questions, just like you would in a real job.
• Expect to deal with missing information. The interviewers might want to see how you ask questions or bring up problems.
• Usually, data scientists follow a similar process, which is: Form the problem statement, gather data, conduct exploratory data analysis, determine algorithm type, execute algorithm comparison, deploy final model or models, and summarize results/lead discussion.

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Ace Your Next Interview

As you can see, none of these questions or tasks were particularly technical, but that doesn’t mean they’re not important. These questions and how you answer them are incredibly critical to your interview, as well as your real job. Being an all-around data scientist does not stop at coding and statistics, but also includes problem-solving, business aptitude, product aptitude, stakeholder or customer communication and passion.