Sat across from the interviewer for your dream job, you may start to feel the pressure. A sure-fire way to quash the interview jitters is to prepare as much as possible. Typically, you can segment the types of questions you’ll get asked in a data science interview; things such as statistics, programming and technical ability, business acumen, and culture fit assessment. Studying up on these will help you prepare as best you can.
Here are some examples of what you could expect when interviewing for a data science role. Tailor these in accordance with what the job description asks for, read it thoroughly and get clued up on the desired points!
'What ML techniques do you work with? / Are these research level or production level techniques?'
What techniques and knowledge are required for the role? Your experience should match up with what is being asked for in the description if you’re at interview level, so make sure you go in with examples of your experience with these.
Try memorizing 3 different examples of where you have used particular techniques and the effect that they have had. For example, if the role requires convolutional neural network experience, prepare 3 examples of projects where you have worked with CNN and the impact they had on the business or research you’ve contributed to.
'Give me an in-depth example of projects you have worked on from inception to completion. What was the project? How did you approach the problem? What was the end result? etc.'
Be prepared to explain your experience and impact in granular detail!
-Why the project existed.
-Your part vs other people's role.
- Provide a step by step walk-through of what you did, what tools and techniques you used.
-End product and what it meant to the business.
Know your own cv inside out, don’t be caught off guard by questions on experience or a project that you cannot dive into and explain thoroughly!
'What’s your favorite algorithm?'
This is a tough one and which algorithms and tools you use will be totally dependent on the job you’re working on. The best approach to a question like this is to have an answer ready before going in. It should be fitting to the role you’re going for, rather than trying to think of a ‘favourite’, think of the most relevant and be able to talk about it. Show that you’re able to make a decision (this is also what they could be trying to figure out), and communicate your reasons for your choice, all the while framing it to what they will desire in a candidate.
'What level of experience do you have with [programming language]? What do you do daily with [programming language] and what were your hardest challenges with this?'
This is a great way for interviewers to measure you up alongside other candidates in terms of technical ability. The programming language they will more than likely ask you about will have been named as a requirement in the job description, so make sure you go in with your answer on this ready to go. Have an example up your sleeve and be able to frame your use of the programming language in terms of how you could use it similarly in this role. If you’re not well versed in what they’re asking for, be honest and show your willingness to learn.
'What is the largest data set that you have processed? How did you approach this, and what was the end result?'
Again, with questions like this, interviewers will be looking for a deep dive into your successes with processing large data sets, your understanding of the approach and techniques used, and how the results have benefited the company. Can you quantify your results in terms of costs, revenue and time saved? If you can, make sure these are front and centre in describing the impact you had.
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.