Soft Skills Is What Sets You Apart in Your Data Science Interviews
Soft Skills Is What Sets You Apart in Your Data Science Interviews
How to up-level your structured problem solving skills and communication skills
How to up-level your structured problem solving skills and communication skills
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:
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).
Ability to translate business problems into data science problems — case study
This portion of the interview is usually a case that’s very similar to the consulting case interview, with a slight data science flavor added on top. It’s usually a RCA (root cause analysis) type of question for a metric movement or business decision. Examples are questions like “our daily active user decreased by 10% in the past week, how to debug what the cause is?” OR “we want to put electric chargers for our fleet around SF, how should we make the decision about where to put them?”
What is it testing? These questions are testing your ability to come up with a problem-solving framework and your ability to explain the framework. It’s extremely important to have one in your day-to-day work as a data scientist so you can make sure that your solution is thorough, comprehensive and can easily make sense to others.
What do interviewers want to see? Keep in mind there’s no right or wrong answer for this kind of questions, only organized or unorganized ones. The interviewers really want to see how you organize your thoughts and walk people through the problem-solving process. So make sure you verbalize all the assumptions you are making, all the details you are exploring and ask all the clarifying questions you might need to ask.
It’s also important to show your ability to take feedback and work with others. Sometimes your interviewer will give you some guidance or hint about where they want you to take the case
How to prepare? The best way to prepare for a case interview is practice, practice and practice. I suggest getting yourself one or two books related to consulting case interviews or PM case interviews, books like cracking the PM interview and case interview secrets come to mind as recommendations. But just reading through the examples the trying to force memorize the frameworks in the books won’t help as much. The most effective thing to do in my opinion is to take sample cases in the book and mock the case with a friend. Very likely your answer will not be identical as the “sample solution”, but ask your friend to check “is my thinking process easy to follow?”, “was it clear how to arrived at each step in the process?”.
Cultural fit — communication skills and working style
What is it testing? Interviewers want to see that you have the qualities that the team is looking for aside from hard skills. And if the interviewer is a hiring manager, they would want to see that they can effectively communicate and work with you.
What do interviewers want to see? Depending on the team, interviewers/hiring managers may care about/want to test different things. But in most cases, I have seen interview questions aiming to prob about your ability to learn, to adapt to changing scope, to collaborate with others and to manage stakeholders.
How to prepare? A couple things can be helpful for this portion of the interview. 1) Make sure you are super familiar with any project you have on your resume, because the interviewer will likely ask about details of different projects on your resume to get a sense of your working style. And 2) make sure you practice the top-down communication style and follow the STAR framework when talking about your projects.
Finally, always remember, interviews are a two-way assessment; you are gauging the company’s fit for you as much as they you, and the behavioral portion of the interview is the best way to test whether YOU like THEM. The people who interview you will likely be the group of people you work really closely with; so if you feel like the “vibe is off” (which trust me, happens more than you would expect), maybe it’s a good signal you should take into the consideration when deciding between offers.
Preparing for a data science interview and want to read more about interview preparation? Here are some articles you might enjoy!
Acing the ML Portion of McKinsey Data Science Interview
A detailed guide for the what, why, and how of the ML part of consulting interviewstowardsdatascience.com
Productivity Tips for Data Scientists
How to work better, smarter but not necessarily harder as a data scientisttowardsdatascience.com