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Episode 16 of Data Science and AI Weekly is here! Tune into this podcast to find You can follow Episode #16 of Data Science & AI Weekly below and learn what not to do in Data Science interviews and what to watch out for during your interviews.


[00:20] Series Overview
[00:31] Topic of Discussion: What not to do in a Data Science Interview?
[01:15] Mistake #1: What not to say!
[02:36] Mistake #2: How to deal with a mindbender? 
[04:16] Mistake #3: Devil in the deets
[06:38] Mistake #4: How to address change in domain?
[07:07] Wrap up!
[07:11] Learn more about Data Science at

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Are you planning to appear for a Data Science interview?

And are you looking to get a Data Science job?

Hi, everyone. Welcome to Episode 16 of Data Science and AI Weekly. My name is Manav. I am the Chief Data Science Mentor at INSAID. And this is Data Science and AI Weekly episode number 16. If you have not subscribed to our channel, just subscribe right away so that you don’t miss out any update or you don’t miss out the next podcast anytime whenever that is getting released. So let’s get started with another interesting episode. We have it for this episode, which is what not to do in Data Science interview.

So the reason this topic is close to my heart is there are some frequent mistakes I see Data Science candidates who are appearing for data interviews keep on making and if you avoid this mistake, trust me 50% of your job during an interview will become easier. Right? So, the first mistake, I’ll talk about three particular mistakes to avoid that I see a reoccurring very, very often number one mistake.

So, the number one mistake is bluffing to the recruiters that you were you are to the interviewer that you were earlier working in a Data Science told lot of people have been taught especially in India that you will get a job if you tell the recruiter or if you tell an interviewer and if you lie about your previous experience and some people what they do is they just say that yes, I was working as a Data Scientist, some even go to the extent of faking their designation. Some go to the extent of lying that I was working on actual Data Science projects. Please do not do that. interviewers interviewing you Right, at least expect this level of honesty from you that you should be truthful. And secondly, what you also need to understand is that the people interviewing you are smart people, right? They can figure out it very easily right? Whether you have actually worked in a Data Science role or not right? So just be honest as possible, right? The more candid you are, the more honest you are about your previous experience.

Right the more confident you will be at the same time the interviewers will also appreciate that that’s the first mistake to avoid Don’t laugh in the interviews. The second mistake to avoid in Data Science interviews is to not ask questions when you are being given a particular case study sometimes taught especially in India that you will get a job if you tell the recruiter or if you tell an interviewer and if you lie about your previous experience and somwhat happens in Data Science interviews is that you might get a mind-bender is say let’s say that some question is asked to you in which you are to guess, estimated thing, for example, let’s say you might be asked a question like, how many cars are right now in parking garage garages across Mumbai, right now that’s like a totally. This is called a mind Bender, which is not totally related to Data Science but helps a recruiter see your problem-solving skills.

Now, when you get a question like that, right, you should obviously solve that in a structured way. But don’t hesitate to ask the interviewer the assumptions that you are making and if you are headed in the right direction, right. So you’re, you are not being tested on the exact answer or the right answer because there’s no right answer. Nobody knows the right answer to these questions, but what you’re being tested on is the approach that you’re taking. So follow a very step by step logical approach as long as you’re following this logical logical step. By step approach, that is good enough, right? So that’s a second mistake to avoid, which is not asking question and getting scared that if you ask a question you might not come across as smart. Now, third question. The third mistake is a mistake that a lot of candidates make. And this mistake is that when they appear for their interviews, they are not prepared for questions related to their project in-depth, right. So for example, there might be a question about which is the most challenging project that you have worked upon? Now you might have worked upon this challenging project six months back or nine months back and you might have forgotten something about it some of the things about it, that’s okay. But at least you should be able to present it in the form of a story what is the problem statement? Why is it was challenging what was the challenges it’s etc. Right. So similarly, you might be get a question, what was the shortest project that you’ve worked on? What is the longest project that you’ve worked on? So all of these are questions that you should practice well beforehand. If you have not practice these questions, they might stump you. And they might not, they might, they will not help you present yourself in the best possible way.

Right. So these are the three mistakes that I recommend that everyone should be careful about while appearing for Data Science interviews. The last mistake is a mistake again, that I see very often that candidates make that they are appearing for Data Science interviews, and let’s say that they are coming from software development background, they do not draw the logical conclusion that why a company should hire them as compared to hiring someone who’s already working Data Science tool. So this applies to, for someone who was switching their industry, or let’s say, who was switching their domains, for example, you might be working in testing right now and you’re looking to become a Data Scientist.

So you should be very clear that how you got started in this, why this excites you how this is something that you think that you will be able to excel in. So if you want someone to believe in you, first of all, you should show that commitment that you’re committed to becoming successful in this, it should not be just that since everybody is talking about Data Science. So I thought that I will also do Data Science. And here I’m appearing for an interview so that commitment to succeeding as a Data Scientist is a must. And sometimes I see candidates not showing that commitment in interviews, right. So this is a bonus mistake that you should avoid for in Data Science interviews.

I hope through this episode, you would have gotten some ideas about some of the pitfalls to avoid. If you liked this episode, leave your comment in the comment section just like this video. This was Episode 16 of Data Science and AI weekly. This has been turning out to be one of the longest podcast series that I have ever done. And if you like Data Science in a weekly CD, just subscribe to this channel. And also all the videos that we have done in the past if you’re watching this series for the first time, the link to all the videos is there in the playlist, go to the description, see the playlist and tune into the rest of the episode. Thanks for tuning in. This is Manav, I’m signing off now. Thank you very much.


Content Writer @ INSAID. A machine learning buff who loves to read, write and explain everything AI!

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