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So you have brushed up on ML concepts, practiced Python and SQL for months, you think you are done with interview prep. But you might be missing the most important and hardest-to-prepare-for part of the interview — problem-solving skills. And based on my experience of interviewing others as well as going through interviews myself, I can confidently tell you, this part is more than often what makes or breaks your interviews.
This is the fifth article for the data science interview guide, in previous articles, I have mostly touched on technical concepts that commonly show up in interviews. For reference, the previous articles are listed below:
- Part I. Distribution
- Part II. Probability
- Part III. Basic Supervised Learning Models
- Part IV. Random Forest
But contrary to most people, I don’t believe technical portion of the interview is the most difficult part; I think the soft skills are the hardest to grasp, learn or teach when it comes to interview prep. As a manager, that’s the most important thing I look for when I’m hiring for my team. Because let’s face it, technical skills are easy to brush up on and learn (not only for humans, but also for machines — ChatGPT can implement code pretty well these days), but the ability to understand, solve business problems and communicate effectively is not something you can build up in a day or two.
This portion of the interview is also the most unpredictable as there’s less of a template interviewers follow; instead, a lot of times the interview would go in the direction you take it. Before you get discouraged, I have summarized a couple of ways the soft skills are tested in interviews and tips that can help you prepare for this part of the interview.
There are in general two modules of the interview that fit into the soft-skill category — case study (which is to test your ability to carry out structured problem solving) and behavioral interview (which tests your communication and again, structured thinking ability).
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