Interview Prep: 10 Things to do Before Your Data Science Interview
If you’re used to spending your days with your head buried in massive datasets, thinking about an interview—and particularly one where your soft skills are evaluated—can be intimidating. But as a data scientist, an important part of your role is translating complex numbers into actionable insights for non-technical colleagues, so demonstrating both hard and soft skills in your data science interview is crucial. These ten things can help you prepare for both aspects of the interview.
Leverage online resources
A plethora of online interview prep resources are at your fingertips, so be sure to take advantage. From practice problems to worked solutions, spend some time on sites like DeZyre, KGnuggets, Udacity, Quora, Data Science Central, Galvanize, Ucanalytics, Sanfoundry, and SAS.
Be fluent in data science trends
Companies want to hire people who are enthusiastic about growing their careers in data science, so it’s worth making sure you’re up-to-date on what’s happening in the space—not to mention, it’ll decrease the chance that you get flustered by an unknown term or concept during the interview.
Know what’s coming
The typical interview process for data science roles consists of 1-3 screening conversations followed by an all-day onsite, so don’t be surprised if you have to speak with a number of people before going into the office. And if you’ve never done and all-day onsite before, be sure you’re mentally prepared for some grilling—both on your technical skills as well as your ability to interact well with others.
Practice explaining data topics in English
Even if your interviewers are data scientists themselves, they’ll want to know you can speak about your work in simple terms. Practice explaining statistical jargon to a variety of audiences—engineers, designers, business operations, etc.
Be ready for a whiteboard
There’s a good chance you’ll be asked to write in front of people and/or talk through your thinking out loud, and it’s worth practicing both so you’re not caught off guard.
Practice positive body language
It can be easy to forget about body language and verbal cues when you’re concentrating on a technical problem, but practice can help. Before your interview, make an effort to take note of any mannerisms that might not work in your favor—upspeak when you’re unsure of something or folded arms when you’re listening to someone speak—and do your best to avoid them during the interview.
Think about how you would approach other data products
In preparing for your interview, it can be useful to consider how you would go about developing features for another product, such as Uber Surge Pricing or Facebook People You May Know. Not only are these types of questions common—thinking through the answers can help you be faster on your feet if something similar comes up.
Evaluate cultural fit
Most companies put cultural fit at the top of their lists when it comes to evaluating potential hires, so it’s worth considering whether you’d be a good fit—and, if so, why. This works to your benefit, too, as you don’t want to accept a role with a company you won’t enjoy working for. Ask culture questions based on what you’re looking for in an employer. For example, if it’s important to you that the company has a strong technical culture, ask whether they host hackathons or if the team contributes to open source projects.
Get excited about the opportunity
If you’re not excited about a potential role now, there’s much less of a chance you’ll continue to enjoy yourself there—so it’s important for your enthusiasm about the company and role to show through during the interview. Familiarize yourself with the company’s tech stack, product, and vision, and consider which elements of each excite you—and be able to talk about it.
Know who you’re interviewing with
A good fit with your interviewers is important for any role, and knowing their backgrounds can help you to guide the conversation. If you haven’t been told who will interview you, don’t be afraid to ask—it’s a perfectly reasonable request.