It can be difficult to know what to expect when going for an interview. Data Science interviews will require candidates to answer technical questions, and often take on technical exercises depending on the company and role you’re going for.
But often overlooked is the talk about soft skills, such as communication skills business savvy, creativity and impact your work has had in the past.
Here is a selection of non-technical questions you could get asked to help you prepare for your next big interview!
“Why do you want to work for this company?”
A big one is why you want to work for the company you are interviewing for. Before your interview, read up as much information on the company as you can. Take a look at their latest news and blogs. This won’t just help you get clued up about the company and what they do, but whether you will be a good fit. Social Media is often a great way for gauging a company’s culture too. This isn’t something a lot of people prepare for or even think about, it’s easy to get wrapped up thinking about the technical aspects for an interview, whereas knowledge on the company itself can really make you stand out.
“What are your strengths and weaknesses?”
Please never, ever tell an interviewer that your weakness is that you work too hard/are a perfectionist. No one believes this. No one ever has, no one ever will. You’re human (for now) and you need that to come through in your interview. This question is actually a great time for you to shine even though it’s talking about a negative. Know your strengths and be able to show an example of how they’ve had a positive impact. When it comes to addressing your weaknesses, show self-awareness and tell them what this is, but highlight what you are doing to improve. This shows your willingness to learn and develop… attributes companies will find favourable!
“How do you handle working with non-technical colleagues on a Data Science projects?”
Communication is key in data science, and you will need to be able to demonstrate your ability to explain technical data insights to your non-technical colleagues. If the data is not understood, it won’t be able to effect change, and the interviewer will need to see that you can indeed make an impact on the business.
You’ll need to be proficient in showing how you can communicate and translate the data into something that everyone will appreciate. Have you done this before? Show examples of when you’ve done this, and how it’s helped shape business decisions and projects.
“Where do you see yourself five years from now?”
This is your time to shine. Talk about your wider goals, your desired career progression, and passions. Interviewers will want to hire people who are ambitious, so it’s perfectly okay for you to be honest and say if you want to be in a more senior role than the one you’re interviewing for at this stage. Most importantly to remember here, is to try and contextualise your desires with the company’s you’re interviewing with. What are their values? What are they utilising their tech for? What is their overall cause and how can you still be adding value to this in five years’ time?
“Who do you most look up to in the data science community? What are your favourite websites/blogs/companies in data science?”
So loosely translated, if an interviewer asks you this, they want to know about the qualities you most admire in your peers and what your passions are within the field of data science. Try not to convolute your answer, opt for someone who will be relevant to the role you’re applying for. Or, a website where the content is reflective of the technologies you will be working on, or industry you will be working in. Whatever you go for here, make sure it is relevant. Other things to avoid are clichés here – the Dalai Lama may well be an inspirational icon, but he won’t help you land a job at a hot new tech startup.
There is, of course, no one size fits all when it comes to data science interviews, questions, and tasks but hopefully, this guide can go some way in helping you know what to expect broadly speaking.
