Data Science

Don’t Do These 3 Things in Your Next Data Science Interview

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From my participation in data science interviews, I have experienced a variety of applicants who have exhibited some things that they should have and some that they should not have.

For this article, we will discuss what to avoid on your next data science interview (some of this can also be applied to non-data science interviews). Below, I will give the top examples that I personally think are things you should avoid in your interview, as well as what to do instead.

Pretend to Know the Answer

Photo by Green Chameleon on Unsplash [2].

I have definitely been a culprit of this ‘do-not’ before when I interviewed. A possible reason for pretending to know the answer is fear of rejection. However, the opposite is really appreciated, is when you either attempt the answer for what you actually do know, or you completely say that you simply do not know.

It might be a surprise to some, but I would rather someone say they do not know something than pretend, lie, or circle around the answer.

It really is okay to not know everything, and some interviewers might be just gauging your knowledge not to quiz you, but to know what they need to teach your moving forward. Also, it is nice knowing you have been completely honest and straightforward in your interview, so that come the time that you do get the job in that case, all expectations are set on your knowledge base.

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If you show that you might lie now, or pretend now, and someone finds out later, then that might leave a bad taste for future work.

What not to do:

  • Pretend, lie, or circle around the answer

What you should say:

  • “I can tell you I do know this, but I do not know the rest”
  • “Here is what I would do when I do not know the answer in a professional setting”

Overall, for me personally, I find it more commendable to hear someone say I don’t know. I do not think it is fair for an applicant to know everything. Of course, there is a subjective limit to the amount of IDK’s, but in the world of data science, there is so much overlap between data analytics, data engineering, software engineering, machine learning, and data science itself that it is simply okay to not know every concept, library, or skill.

Note: this might be just my preference, but I think honesty and transparency can go a long way and is something I think we are all always working on.

Going on Long Tangents

Photo by Denys Nevozhai on Unsplash [3].

Sometimes people may think answering a question longer, means they sound like they have more knowledge. The opposite is most likely true, when having a concise and confident answer, the interview will know that you knew the answer right away, and can move on to test more of your knowledge. It can also be hard to follow when someone talks for minutes on end.

What not to do:

  • Delivering an answer to a data science project that takes longer than say a few minutes
  • Tip: a longer answer does not mean a more impressive answer

What you should do:

  • Create a user story around a project with data science by highlighting the business problem, constraints, pros and cons, and impact from a model (for model answers)

To summarize, be concise so that the interview can be more of a conversation.

Centering Data Science Around You

Photo by Javier Esteban on Unsplash [4].

This ‘do-not’ might sound a little vague. To be more clear, I am talking about when you only talk about a data science project how it has pertained to you. Of course, the main parts of your answer should show off your experience and skills, but there is a major portion that should cover others.

What not to do:

  • Only talk about a data science project in regard to you
  • “I got a model accuracy of 98% which was really good for me”
  • “I primarily worked on the project myself” (it is okay if this is true, but if there were more people involved, then see below)

What you should do:

  • Mention others involved in the creation and finalization of the model
  • Discuss stakeholder collarbation involved
  • Discuss different departments that benefitted from the model
  • ‘‘I got a model accuracy of 98% which was really good for the company, allowing for not only our KPI’s to improve significantly but also created a better product for our users”
  • “I worked on the model many different coworkers, including my main stakeholder, the product manager, as well as software engineers and UI developers for the full model implementation into the product”

Overall, you might think it sounds more impressive to say you did everything yourself, but that might show the interviewer the opposite. It might how that you are hard to work with, and not experienced in collaboration.


As with any opinion-type article, take advice with a grain or a few grains of salt. You can impress interviewers in different ways, rather than trying to do what you think they want to see, be yourself, be honest, be open, talk through problems, be concise, and thank and discuss the people who helped you get where you are.

Here are three things to avoid in your next data science interview:

* Pretend Knowing the Answer* Going on Long Tangents* Centering Data Science Around You

I hope you found my article both interesting and useful. Please feel free to comment down below if you agree or disagree with these things you should avoid in your data science interview. Why or why not? What other things do you think you should avoid in data science interviews? These can certainly be clarified even further, but I hope I was able to shed some light on data science interviews, and interviewing in general, Thank you for reading!

I am not affiliated with any of these companies.

Please feel free to check out my profile, Matt Przybylaand other articles, as well as subscribe to receive email notifications for my blogs by following the link below, or by clicking on the subscribe icon on the top of the screen by the follow icon, and reach out to me on LinkedIn if you have any questions or comments.

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Sr/MS Data Scientist. Top Writer in Artificial Intelligence, Technology, & Education. Towards Data Science.