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What should you expect in a data science job interview and how can you prepare for one? We asked a Senior Career Advisor at Metis Data Science Bootcamp, Andrew Savage, to give us some insight into what candidates will experience when they apply to data science jobs. Find out examples of interview questions you might get asked (and what you should do if you don’t know the answer), the soft skills interviewers are looking for, and see Andrew’s favorite online resources to help you ace the data science job interview. Watch the video or read the blog post!

What are the components of a data science job interview?

Generally, the data science job interview involves three different steps:

1. A phone screening with a recruiter, someone from HR, or hiring manager. 

2. Coding challenge

3. An onsite interview with a team lead, the data science manager, or a senior data scientist.

Should every data science job interview include a technical portion?

There should always be some sort of technical component that, if nothing else, quizzes your knowledge around technical matters and gets you to explain your thought process. That could mean you actually have to go to a whiteboard to share your code with the hiring manager, or walk through an algorithm. If there's no tech component, I think that is a bit of a red flag. You might want to ask yourself, "Does this company really know what they're looking for and do I match the skill set that they actually need?"

What are some examples of data science job interview questions?

1. A chit-chat question.

Example: "Tell me an interesting technical problem that you solved.”

2. Technical knowledge.

Example: “Describe the differences between supervised and unsupervised learning."

Does programming language matter? If I learn Python, could I interview for R jobs?

A lot of employers say, "We've been doing a lot of work in R, but if Metis grads know how to do their work in Python, then we are confident that they'll be able to learn R.” If a company says “We need someone who's fluent in R right away,” then the majority of our graduates aren't a great fit. Only those with Masters or PhD-level degrees in a quantitative subject are likely to have that previous R experience.

Over time, it's becoming less and less the case at Metis. Generally, I find that only a very small minority of employers, less than 10%, have a strict “R or nothing” requirement. It was more common two years ago, but lately employers usually like Python and R. Usually there isn't the specific need to know R only.

What if an interviewee doesn’t know the answer to a question?

If you draw a blank and you can't answer, it is totally fine to say, "I don't know." What you don't want to do is just blurt out an answer, because it's one thing to be wrong, but you don't want to be wrong and reckless.

A strategy that I often tell my students is to know the nature of the problem. You may not be totally sure how to phrase the answer, but you can begin your answer with, "I don't know exactly what that is, but my interpretation of what it is you're working with, is ...." Or if you get a question like, "Write this out or solve this for me," you can say, "I'm not totally sure the best way to solve this, but let me write out some ideas that I have and some ways in which I might go about doing this."

If you do that, the interviewer will see that you're going out on a limb, that you’re not totally sure about what's going on, and may be willing to watch you work through your methodology and thought process. They’ll usually give you some bread crumbs if you're not going in the right direction.

Do interviewers expect an applicant to get everything correct in order to get the job?

No. In many situations it’s equal parts technical evaluation and behavioral evaluation. Good interviewers and hiring managers know that you can hire someone who is A+ on the technical side, but may not fit from a cultural or a behavioral standpoint.

In addition to getting the right answer, there are also other things that are important. For example, interviewers want to see if you’re able to explain how you solved a question, how you interact with others in the process of answering your question, how you communicate, and whether your tone is conversational or adversarial.

There is almost always room for less than 100%. There's room for, "She arrived at the wrong answer, but her methodology, her approach was really sound and we understood where she was coming from." Sometimes that's more important than acing the tests.

What are the non-technical skills, and the culture-fit types of skills that interviewers are looking for?

Yes, companies are looking to hire talented data scientists, but interviewers are also looking to hire people who they will enjoy working with. It's not simply a technical meritocratic methodology, although technical prowess and the ability to solve problems is super important. Those attributes are:

1. Curiosity and interest.

2. Communicating your thought process is super important.

3. Problem-solving skills

4. Personability is super important.

How can people prepare for data science interviews to make sure they get the job?

The way we prepare Metis students is multifaceted. Yes, we are looking for a baseline understanding in quantitative subjects like linear algebra, statistics, and proficiency in Python, but we're also trying to expand students’ knowledge of topics that we find are most relevant, and that companies are hiring for.

1. Project-based learning and portfolio development

2. Exposure to networks and industry professionals

3. Workshops to review job search best practices

What’s your advice to employers who are hiring data science bootcamp grads?

Be open-minded about who might graduate from a data science bootcamp. At Metis, our students range in age from 19 to 60 years old. Some have PhDs in statistics or computer science and are looking to get into data science, while some have no Bachelor’s degree at all. We do a really thorough job of vetting students, and that's not restrictive with regards to age, background, or level of education. So, keep an open mind, especially towards people who are mid-career changers.

During the interview, please focus on what is actually essential for your employees to perform on the job. A lot of people don't have a lot of background in how to conduct an interview properly and they may ask “pie in the sky” impossible questions to see how people react and squirm and solve something that is completely irrelevant to the job. Make the interview practical.

What are your favorite resources to prepare for a data science job interview?

1. ModeAnalytics


3. Chris Albon

Check out the slides from the video.

Find out more and read Metis reviews on Course Report. Check out the Metis website.

About The Author

Imogen crispe headshot

Imogen is a writer and content producer who loves writing about technology and education. Her background is in journalism, writing for newspapers and news websites. She grew up in England, Dubai and New Zealand, and now lives in Brooklyn, NY.

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