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5 Data Science Interview Mistakes I’ve Made | by Matt Przybyla | Aug, 2020

  1. Introduction
  2. Discussing the same past project
  3. Not asking enough questions
  4. Assuming interviewers know my past experiences
  5. Not considering the business impact
  6. Not overviewing the whole Data Science process
  7. Summary
  8. References

I have interviewed with several companies, having some repeated and key mistakes along the way. As a result, I have learned from them and have reached success by way of job offers for Data Science positions. Some of these mistakes I will highlight pertain especially to Data Scientists, while some can be applicable to nearly any interview as well. My goal is to have you skip the trial and error of interviewing at countless companies so that you can focus on improving and executing an exceptional interview for your goal company and goal Data Science position.

Sometimes, as an interviewing Data Scientist, you will encounter several interviewers that will ask similar questions. I assumed that meant that I should answer with the same answer for each person. I also thought that new questions could reference back the same past project. However, it is important to note that these interviewers will ultimately discuss amongst one another after your interview is finished. What this means is that they will see that you have only talked about the same education or professional Data Science projects for similar questions and for different ones, so it has limited them on how they can judge you. It also seems like you have less experience because you talk about the same project. I was actually called out for this point. They told me I kept going back to the same example and they wanted to hear about other situations and how I handled those in reference to the question.

I do think that saying a similar answer to similar questions is not necessarily a big mistake — in this case, you would want to make sure you add some details for each new person that asks you that same question.

For the other point, looking back, I do believe this mistake was bigger and something you should consider as some important advice. The reason I think this point is more important is that I had the interviewers tell me it was a problem.

I am not sure, however, if all companies felt this way because I tend to become proud of one project and talk about that same one for each new question. I also thought it would be unique to have all these answers that were based on the same example so that the interviewers could get a better sense of my whole process as a Data Scientist.

My mistake was telling it so similarly for different questions that it seemed as I only worked on one project for a whole year. You can imagine that would not impress the interviewers.

It is difficult to say with 100% certainty that this mistake is something you should avoid, but I can tell you that it was brought up to me as a mistake, so it was something that I did learn from and improve on going into my next interview.

Photo by Emily Morter on Unsplash [2].

This point may not seem as unique as the one from above, but it is broader and I believe most companies would agree this point is a common mistake.

Not asking enough questions shows a few things:

  • you are not interested in the company
  • you did not pay attention well enough to come up with a question
  • could show you are overconfident
  • could show you are hard to work with

No one wants to work with a know-it-all that is overly confident. Similarly, no one wants to work with someone who is not interested in the company or its goals and respective Data Science projects. Most importantly, when you do not ask enough questions, it sounds like you did not care enough to listen. Ultimately, what you are portraying as you fail to ask questions to the interviewers, is that you would not be a good candidate to collaborate with.

Also important is that this mistake does not just pertain to Data Scientists, but most career fields as well.

This mistake is something I tend to do a lot, not just in interviews but in storytelling as well. What you are doing with interviewers is just that — storytelling. A common mistake I have made was assuming the interviewers knew some information about the background of my project.

They most likely will know nothing — some will not even read your resume.

That comment might come as a shock to you, but there may just be one or a few interviews from that company who have delved into your resume — that is not to say you should not overview your own resume before going into the interview because they may very well ask you a considerably specific question from one detail of your resume examples.

However, going back to storytelling, you will need to set the scene when answering a question by providing basic information for the Data Science project you performed. This type of explanation will show the interviewers that you can work with stakeholders and other non-Data Science people.

Some key points to bring up are:

  • what was the business problem?
  • or why did you want to do the project in the first place?
  • who was involved? (Product Managers, Software Engineers, etc.)
  • what was the process?
  • how did you do it?
  • where in the grand scheme of the business did this project fit in?
  • what were the results?
  • how were they perceived?
  • how many people did you help/money did you save/time to you save?

Once you outline your past projects in this format, it will better paint the picture of your answer.

Photo by Riccardo Annandale on Unsplash [3].

Going with one of those storytelling points from above, you must make sure to consider the business impact of your model. I believe this mistake is specifically popular for Data Scientists because they focus solely on the model and its performance, but fail to mention how the business was impacted.

You want to highlight your results in a way that is accurate but more importantly, impactful. You most likely have impactful results but you may have failed to let the interviewers know this point. You can phrase your answers like this:

  • “I worked on the Decision Tree model that automated a manual process, saving the process 50% time, and 50% money, creating time and money for bettering that product.”

If you refer to your 99% accuracy constantly but fail to mention its impact, you can expect that the interviewers will think you do not know how to work within a business and are more educational oriented. Sometimes even lower accuracy is better if the overall process is faster and more impactful in some way.

Pretend like you were hiring someone — you would want to know they can help your business.

Like the first mistake in this article, I have also been specifically called out for this one — not overviewing the whole Data Science process. This point means that I did not include a discussion around the Data Engineering and Machine Learning components that happen before and after a main Data Science project.

The interviews want to know:

  • how you got the data
  • how you preprocessed it
  • how the model was changed to object-oriented programming format
  • how tests were made
  • how it was deployed
  • how it was integrated into your product

These points may not be something you have performed yourself, but the interviewers know that every Data Scientist is not also a Data Engineer, Machine Learning Engineer, or Software Engineer for that matter. What they are testing you on is if you were aware of the whole process from start to finish and who worked on what. If you answer this question correctly, then the company will see that you fall into a more specialized Data Science role, and could possibly learn the beginning or end parts of the Data Science process.

Photo by Jonathan Borba on Unsplash [4].

I hope you have learned some truly new advice from this article. I have covered five main mistakes I have experienced in the interview process for Data Science positions. You may or may not experience a job offer because you avoided my mistakes, but they could provide to be important to you in some way. Perhaps, putting your own twist on these points will make the best you for Data Science interviews.

To summarize, these are the five Data Science interview mistakes I have made:

Discussing the same past projectNot asking enough questionsAssuming interviewers know my past experiencesNot considering the business impactNot overviewing the whole Data Science process

Please feel free to comment down below and discuss some common mistakes you have made during the interview process for Data Science roles so that we can all learn from one another.

Thank you for reading!

[1] Photo by Estée Janssens on Unsplash, (2017)

[2] Photo by Emily Morter on Unsplash, (2017)

[3] Photo by Riccardo Annandale on Unsplash, (2016)

[4] Photo by Jonathan Borba on Unsplash, (2019)