Get interview ready with these 5 questions…
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 be 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?”
In a best-case scenario, your experience closely matches the job you are interviewing for. If this is the case, make sure you are prepared with examples of your relevant experience.
Try memorizing 3 different examples of where you have used specific 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.
If you do not have much experience that matches the role, consider what the job description asks of you. Think about where you might have applied the job requirements in other aspects of your life or career. If you are entry-level, did you work on any relevant topics during your education? Have you got a GitHub profile or any notable publications that you can use to back up your competency? Where possible, always provide proof of your capabilities, and the real-world impacts they have had.
“Tell me about 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.
- How your role integrated with other members of the team.
- Provide a step by step walk-through of what you did, what tools and techniques you used.
- What was the end product and what results did it produce for the business?
Make sure you know your own CV inside out. Don’t be caught off guard by questions on experience or a project that you cannot confidently dive into and explain thoroughly in interview!
“What’s your favourite algorithm?”
This is a tough one, as the algorithms and tools you use are dependent on the jobs you’ve personally worked on. The best approach to a question like this is to have an answer ready before going in. Make sure it is 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 ? What do you do daily with 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 languages they will ask you about will have been named as requirements in the job description. 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!
