Embarking on a Data Science Interview? Play to Your Strengths.

Walking into your first interview can be nerve-racking, but a little upfront honesty can help make the unknown more approachable.

Written by Tyler Holmes
Published on Dec. 09, 2021
Embarking on a Data Science Interview? Play to Your Strengths.
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Let’s face it: Interviews can be intimidating. Walking into a room (real or virtual) full of strangers and laying your livelihood bare on a piece of paper invokes a special kind of vulnerability. Add in the fact that it’s the beginning of your professional journey in a complex field like data science and it can be enough to fuel a perfect storm of nerves.

So how does one combat the jitters while still standing out as the ideal candidate? Will attempting to master machine learning, SQL databases and Python overnight be the key to a lifetime of success? Not quite. In fact, according to Adswerve Data Scientist Christopher Wilson, a little mental preparation and natural curiosity can make all the difference.

Coming from a software engineering background, Wilson approached the interview questions by showcasing his software strengths while openly acknowledging where his skills fell short in data science. By leaping over these initial hurdles and focusing on the bigger picture of future data-driven possibilities, he was able to prove to the interviewer — and himself — that he had ample capabilities to grow seamlessly into the role.

“Try to find an approach to solving problems with the skills you have,” Wilson said. “It’s not wrong to ask questions in a technical interview — just because you don’t know something doesn’t mean you aren’t capable of fulfilling a job.”

After all, interviewers want candidates to succeed. That’s why Built In Colorado checked in with Wilson after successfully nailing his interview to discover his biggest hacks for anyone looking to land a great gig as a data scientist.

 

Christopher Wilson
Data Scientist I • Adswerve, Inc.

 

Tell us a little bit about your first experience interviewing for a data science role.

I first applied for a job as a junior data engineer. During the series of interviews I went through, the interviewers realized that my background might be a good fit for a data science position as well. They ultimately decided that my interview process would be for both the data science and data engineer positions. This was a huge blessing because my very first exposure to either of these positions professionally ended up being a side-by-side comparison.

The company I interviewed with ended up first having me meet for a baseline interview, answering questions like: “What's your background?” “Why are you qualified for this?” “Tell me about projects you've worked on.” I guess I did OK because then they came back to have me complete two tests — one for a data engineering position and one for a data science position.

I don’t have a background in data science so I approached the data science task leaning on my proficiency in software engineering. I learned that one should acknowledge and embrace the places where they are strong and weak. Be honest with yourself with what you can and cannot do and try to find an approach to solving problems with the skills you have.

 

What is the most important thing you do to prepare for a data science interview, and why?

When approaching data science problems, I find that it is essential to be able to explain the big picture of the data in plain terms, rather than overly technical jargon. I recommend practicing this before interviews.

For example, instead of saying, “The client would like to understand user propensity to convert based on web browsing history and user demographics,” it’s always been easier for me to say something like, “The client wants to know how likely it is that a user will purchase something based on how they act.” Then I can dig deeper with questions like, “How can I understand how a user is acting with the data I have?” or “How can I mathematically analyze this data that shows user behavior to tell the story that the client wants?”

As you start with the plainer version of the problem, it’s much easier to understand why certain statistical approaches will be better than others because you are able to more simply break down the problem. Don’t get bogged down in the nitty-gritty until you really understand the context and importance of solving the problem in the first place.

Knowing where you might struggle and making that clear to the interviewer will really go a long way.”

 

What advice do you have for someone preparing for a data science interview at your company?

It’s not wrong to ask questions in a technical interview. Knowing where you might struggle and making that clear to the interviewer will really go a long way. Just because you don’t know something doesn’t mean you aren’t capable of fulfilling a job.

Interviewers don’t necessarily want you to fail. Hiring a new team member is a difficult task and if they can save time and energy in interviewing, they probably would prefer that you are a good fit. So, when you go into interviews, make sure you are up-front with what you can and cannot do. Help the interviewers know that you are not going to cause them stress if they hire you because you are willing to work on your weaknesses and be positive while doing so.

Probably most important: Be nice. Think about it logically — wouldn’t you rather work with someone who’s really kind and genuine but might not be a genius, rather than someone who is a genius but really mean? No one wants to work with a know-it-all or someone who is difficult to get along with. Be nice to everyone, even if things don’t go well.

 

 

Responses have been edited for length and clarity. Photography provided by associated companies and Shutterstock.

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