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Data Scientists – Are You Prepared For Your Next Interview?

You’ve perfected your CV, got great experience under your belt, maybe a PhD and can wrangle data amongst the finest but just how prepared are you for your next interview?

Just the thought of the face-to-face interview stage is enough to strike fear into the bravest of us.  Here are a few things to keep in mind and stave off the sweaty palm syndrome. (Bonus tip – if you are prone to perspire through the palm, remember to use a tissue before shaking hands with your prospective future employer!) 

How well do you know yourself?

First things first, prepare to be an expert in YOU. It sounds quite basic, but you need to know all about your work in great detail and know exactly what is on your CV when meeting with a hiring manager. Be ready to provide detail at a headline level, but also be completely fluent in articulating all of the projects you have worked on in your career. It’s crucial to be able to explain the reasons for the project, your individual contribution and finally what the end result/business outcome was.

Think about all of the hard-won problems you have solved in your career; can you confidently say you have articulated them in your interviews to date? Most employers will look at past behaviors and results as to an initial indication of how you will benefit their business, so it’s important that you can project this in your interview.

Also, don’t forget what you have written on your CV too. It’s surprising how many times people get caught out on a question on something they’ve done at the beginning of their journey, or a coding language they haven’t used for a long time. Employers like to know that you are an expert (or as close to) about the things you’ve written you know about.

What’s in your toolbox?

 It’s not just about reeling off a list of projects you’ve worked on. Many employers/interviewers are going to want to know why you have decided to solve problems in a certain way.

One manager describes the interview process as an examination of theory and application. What he looks for is an understanding of “why?”. Why did you choose that algorithm? Why did you choose that technique? Why didn’t you choose to use this equally common method instead? Why did you reach that conclusion?

In this line of questioning, it’s important to know why you do what you do. Not that “well, my manager told me to do it” and more like “my manager and I looked at a range of techniques and we chose this one because…”

Other things to consider is having an understanding of the impact to business for choosing the method/algorithm/technique that you have chosen. Thinking in scale and knowing exactly if the model will productionise. Critical thinking skills are more important now than they have ever been.

What happens in the technical bit?  

Technical testing is becoming more and more common with organisations that want to know if you can back up what you say you can do. You may have some great answers to the how, what and why questions, but this isn’t where the line of questioning ends.

In my experience, technical testing ranges from pre-prepared online assessments (Kaggle, Codility, etc) to on the spot whiteboard and pen sessions. There is no black and white way of preparing for these tests, they will differ in every interview you go to.

What is important is to understand what problems the business is facing. What does the business do? What does their platform/product/application look like? Why might they be applying Machine Learning techniques? What does the job description ask for? These things can normally give some clues and indications as to what the technical testing may be about. If the job description is asking for a Computer Vision expert with solid Python coding skills…this may be an indication of where you’re going to be tested!

Is there anything else to remember? 

Managers will want to know that there is a human behind the prepared answers. What are your ethics? How well do you work as part of a team? Are you likely to clash with existing members of the team? How well can you communicate and present your findings to senior business stakeholders?

Managers will want to know what’s driving your passion in Data Science. Why are you doing what you do and what is the overall end game? It’s becoming more and more appealing for managers to understand that Data Science isn’t just a job for you. Are you submitting papers to be published at conferences? Do you partake in weekend hackathons? Are you part of any online Data Science communities? Sometimes that throw away answer about writing blog posts on deep neural nets can be the indicator to a manager that you genuinely have an interest in what you do.

Hopefully, there are some good takeaway points here. There isn’t a copy and paste method of preparing for interviews, as no two interviews are alike. Be prepared for all eventualities, but most importantly be prepared to be an expert in the most important subject…you.