Here’s Episode #5 of Data Science and AI Weekly! In this episode, we will discuss what to expect in a Data Science Interview and how a Data Science interview looks like. The podcast is hosted by Manav, Chief Data Science Mentor at International School of AI and Data Science.
TIME-STAMPED SHOW NOTES:
[00:10] Podcast Series Introduction
[00:24] Topic of Discussion: What to expect in a Data Science Interview?
[00:57] Stage #1: Telephonic Screening/ HR Round
[01:50] Data Science Case Study
[03:30] Stage #2: Data Science Task
[04:07] Stage #3: Face-to-Face Interviews
[07:00] What to expect next?
[07:23] Wrap up!
[07:43] Next episode preview
[08:35] Learn more about Data Science at www.insaid.co
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Welcome everyone to Episode 5 of Data Science & AI Weekly!
My name is Manav, I’m your host for this episode and let’s do the honors, if you have not already listened to the first four episodes, I would highly, highly recommend you to do that first and once you’re done that, come back to this episode.
This is Episode number 5. In this episode, what we’re going to cover is what do Data Science interviews look like? And this is going to be an interesting episode because what I’m going to help you see is that how does your entire interview process look like because what a lot of us starting our careers in Data Science think, is that it just got to be one interview and we are going to be done. It’s not like that. Let’s dig deeper into what a Data Science interview is all about.
So there are 3 stages of what a typical Data Science interview looks like. The first one is the screening round. So, in this round essentially, say a job has been posted on LinkedIn & you applied for that particular role. HR calls you if they find your profile suitable. The company is looking for a Data Scientist position and you meet the skill-set, HR will call you and they will screen you whether what you have written in your resume and what the company wants has a good overlap between the two.
If the HR feels that there is a good overlap between the two, then you will be called for or interviewed. On the Interview Day, what ends up happening is you don’t directly go for a face-to-face interview, right? You might as well but most companies would not directly meet you for a face-to-face interview.
You will be given a problem statement or a case study and a Data Science case study. Let’s say that you report for the interview at 9 am or 10 am, you will be given, let’s say, 3 hours or 4 hours to solve that particular case study and depending on whether you have gotten the final result right, and whether there is evaluation, for example, you have been evaluated given that this was a problem statement, this is how you’ve done, if you’ve done horribly bad, right, you will be asked that your interview is over.
If you have done reasonably well or if you have done really well right you will be called for an in-person interview after that, right. Now, a little twist to some of the interview process is that in a lot of interviews, you are not given a task on the day of the interview. You might be given a task before the interview/ before calling you for an in-person round itself.
That is you will be given an assignment to do at your home. And you will be given, let’s say 24 hours, to submit that assignment right. Now there what I recommend you is to not take any assessment from any other person who is expert in the space, because in the end, even if you do that task by taking help from some other expert, you will most likely be figured out in the interview. So, you would want to possibly read a little bit about the task on the internet if you need some help.
But ideally, I would recommend that take this as a screening process yourself. If you are not able to do that task, right? Most likely, you’ll not be able to clear the interview. So you might get a home assignment as well.
You’d usually get 24 hours to submit that assignment and depending on how well you have done on that particular Data Science task, then the company might decide to call you or decide to drop you at that stage. Now, this is the second stage. So the first stage was screening, the second stage was a task.
The third stage is going to be an actual interview. And this is the 3rd Stage, but this is what a lot of candidates think is the only stage, which is not the case. There are two other stages that I’ve already discussed. In the third stage, you’re going to have an interview with most likely the boss that you’re going to be reporting to, who might be a Senior Data Scientist, he might be Head of Data Science, he might be a Chief Data Scientist, or he might be a Data Scientist itself in your team.
If it totally depends on what level of Data Science maturity the company is at, right? For example, if the company already has 10s of Data Scientists working in, then you might be recruited, first of all by a Senior Data Scientist, and there might be another round of interview with the Chief Data Scientist, or sometimes what can also happen is that there might be a panel of 3 people and all the your interview would be one round of interview only, right?
So now let’s see what type of questions are asked? So if you have only one round of interview after you’ve submitted the task, then in that round, the focus is going to be on 3 things, first of all on knowing you because they are interested in knowing more about your profile, the kind of work that you have done.
Secondly, they’re going to be interested in your knowledge about Data Science overall, why you’re in this field? What excites you? Are you aware of the trends and some of the questions regarding algorithms, etc? Which is your favorite algorithm? and you might be asked more than that.
And the third part of the interview is going to be focused on the task itself that you are submitted. Now, in some companies, what ends up happening is that because of the 3 parts to the interview, the interview ends up becoming very long. That’s why you have two stages of interview, in which let’s say, a senior Data Scientists in the first round of interview focuses on the task part and sees your approach in the task and that in itself might be 30-35 or 40 minute interview, wherein the interview might start with, for example, you did this task.
First of all, what did you understand? What is the objective of the task? How did you approach it? What are the possible solutions you thought about? What kind of machine learning algorithm you applied? Which machine learning algorithm did you decide is the best fit? Can you give me some other example of how this problem could have be solved etc.
So, essentially the entire interview would be about the task and this is very, very common, it might take 40-45 minutes and sometimes it can run all the way up to 60 minutes. Now, if this task-based interview goes well, then you would have the next round of interviews which is focused more on your career, most about your profile, most about your fitment for the role.
There will be questions like have you researched about the company? Your typical standard interview questions nothing dramatically different but focused on your fitment for a Data Science role, right, but as I said, if the company does not want to focus too much on the task, both these interviews can also be merged into one. So this is the interview round.
After that, you finally get your offer letter based on whether you have actually cracked the role or not. So essentially, just to summarize, it’s a three-stage interview process that you need to go through. You need to research each one of these stages so that you are prepared for the phone-screen, prepared for the task, what kind of tasks come, there are a whole host of tasks that you can get if you practice for these tasks, cracking these tasks becomes easier.
And the interviews as I said, I will possibly do a podcast on the kind of interview questions you should expect. This podcast was more about the process so listen to this episode that I will be doing soon on the kind of interview questions that you can expect in a Data Science interview.
Alright, so that was the interview process of Data Science interviews. I hope you loved this episode, I had a great time sharing my insight about this. If you love this episode, leave your feedback. Leave your comment. I’m always looking for ideas on topics that you would want podcasts to be made on.
And if you loved this episode, make sure that you listen to another episode and do share this episode with anyone that you know in your circle can benefit from some of the steps that I’ve shared in this interview. Right. So thank you very much for watching this episode of Data Science & AI weekly. My name is Manav, I’m signing off and look forward to you tuning into another episode.