How to Prepare for a Data Science Interview: 5 Tips

Do you have an upcoming data science interview? 

These interviews can be challenging for a variety of reasons.  

If you’re a little out of touch with the basics of data science, it’s easy to get nervous because of the fear of being unable to accurately answer all your hiring manager’s questions. It’s possible to fumble even an entry-level data science job interview because of a lack of confidence.

A data science interview can also be intimidating because of the live coding exercises involved, which can vary hugely making it unclear what to expect. It may also be challenging because of improper research.  

In fact, a TimesJob survey found that 50% of candidates failed interviews due to poor research and preparation. 

So to ace that interview, knowing how to prepare correctly for the specific position you’re applying for is important.

In this guide, I’ll be sharing tips on how to prepare for a data science interview to give you the best chance of walking away with your job offer.

Let’s get right into it. 

1. Determine the Job Responsibilities 

What will be your responsibilities for the job? 

The field of data science is hugely diverse. 

Without knowing the exact details of your job responsibilities, you could walk into a data science interview without a clue about the requisite skills. 

The Dice Tech Job Report established a 50% increase in the demand for data scientists. This surge has reverberated across many new industries, from telecommunication to healthcare, creating many different roles. 

While niches commonly revolve around programming, machine learning, and data processing, your workflow can vary significantly from one position to another. 

Therefore, you need to, first of all, determine what type of data scientist job you’re applying for.

For example, some different job roles in this field include

• Data visualizer
• Machine learning engineer
• Data engineer

This is only a brief sample as there are more than 10 different roles you could fill as a data scientist. 

In a nutshell, knowing the exact positional demands is the best way to prepare for a data science interview. 

For instance, say you’re interviewing for a data visualizer position. 

According to the IEEFA, as a data visualization specialist, your job description entails being able to visually transform data sets into insightful visuals. 

If you have little experience with data visualization, then it’s prudent to start getting acquainted with tools like FusionCharts, Tableau, and Grafana. 

Most job descriptions will be specific about resources they’d like you to be familiar with. In the sample above, for instance, the position requires you to know your way around Adobe illustrator. 

A crash course is a good option to quickly get you up to speed. 

2. Review Your Resume Projects

Can you back up the projects on your resume?  

A Zippa survey involving more than 1,000 U.S. respondents found out that 30% of job candidates lied on their CVs. This entailed applicants claiming to have bigger project roles than they actually did.

In the same breath, it emerged that the majority of recruiters won’t hire you if they believe you’re dishonest. 

If you can’t clearly explain your design process and don’t even remember the code for the project, then you may not make the best impression. 

The hiring manager may conclude that you may even have not been a part of the project at all.

So to know how to prepare for a data scientist interview,  revisit your design projects, and try to rebuild everything from scratch. 

Basically, you should be able to explain the modeling techniques you used, regressions, random forest, etc., including assumptions you’ve made for the model and why you chose it in the first place. 

Let’s say you stated you’ve built a sentiment analyzer.

In this case, you’ll want to be able to talk about the tools you used and why. If you used GraphLab, you could point out that you did so because of its ability to support several data sources. 

Moreover, instead of only being able to walk your recruiter through your code templates, you should be able to recreate the code afresh should your interviewer require a demonstration. 

3. Research Past Interview Questions

Have you read up on past interview questions? 

If not, you’ll be going in blind and you may end up coming off as uninformed to your prospective employer, which is something that could really hurt your chances. 

Glassdoor recently surveyed 750 executives in both the U.K and the U.S. to determine what they were looking for in the ideal candidate. 

It emerged that only 12% of decision-makers will consider you for that data science job if you don’t appear well-informed.

Consequently, you want to avoid getting caught unawares by doing your research, which is the most important tip on how to prepare for a data scientist interview.

So what’s a great place to get up-to-date data science questions? 

Well, there are multiple options you could consider here. 

First, some job boards offer great data science communities where you could actually learn from other candidates who’ve taken similar interviews. 

Interviewees post their experiences and data science interview tips in a Q&A thread to give you a heads up.

While the questions won’t always be the same as variables are bound to change, the underlying concepts are great when learning how to prep for a data science interview.

As an example, Glassdoor has a data science interview forum you could try out. 

This forum contains more than 2,200 real-life data science interview questions, centering around queries on SQL joins, probability, and python programming, which are among the most popular test areas. 

Secondly, you could also try out a specific course on how to prepare for a data science internship interview on a reputable e-learning platform. 

4. Refine Your Product Sense

Product questions are inevitable in your data science interview.

At the end of the day, your job will involve solving actual consumer problems, and the company is ultimately looking to learn whether you can chip into its cause by building great products. 

If your product sense is off, your recruiter might take this for a disconnect between your skills, practical knowledge, and the logical skills the job needs. 

A Job Outlook survey by the NACE uncovered that only 55% of candidates were proficient in problem-solving. Given that 99% of employers consider problem-solving a critical skill, many candidates are missing out as a result.

It is therefore important to refine your product sense to best know how to prep for a data scientist interview.

These often include metric-related impacts and a theoretical product design scenario.

Product building questions are the most challenging because they are open-ended. In such cases, there’s usually no single right answer your recruiter is looking for. 

Let’s consider this Situation, where Lyft is asking you to create a heatmap to guide drivers to improve customer discovery. 

Here, client density, location, and traffic patterns may be great starting points for your data set. These may fuel a time-series forecasting model to anticipate customer demand.

That said, your employer will mostly be keen on your thought process involving why such a feature would be beneficial, the possible KPIs you could set, and how you could solve an edge case or exception.  

5. Diversify Your Coding Practise 

Are you in touch with your programming?  

Aside from your project coding, you may need to refresh your overall coding skills. That’s because your data science interview is likely to contain coding exercises to test your suitability for the position. 

If you fail test exercises, chances are you’ll not make it past the interview. 

According to a McKinsey survey, 43% of employers today said they find candidates don’t have the skills they say they do. This is why your hiring manager will need to see a demonstration of your expertise during your interview. 

Coding tests for your interview can come in either one of two ways. 

Via CodeSignal and HackerRank, among other integrated development environments that your recruiter prefers, you may be required to take a timed coding test online. This typically involves Python but R and other languages can feature as well.  

If you’re only proficient in one language, here are some data science learning resources which are the best way to prepare for a data science interview. 

Additionally, you may also be needed to perform a live coding session.

LeetCode can come in handy for your practice sessions for a live test. This platform allows you to code on over 190 questions and gauges your answers against the correct ones. 

If a simpler code solution exists to solve a problem, it’s wise not to take the long road to earn favor. As long as your code runs, and you’ve demonstrated your process in clear and cornice steps, your interviewer will be impressed.

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Conclusion

Are you familiar with how to prepare for a data science internship interview?

It’s important to keep in mind what could go wrong to ensure that everything goes right. Your resume projects could be the source of your undoing, and so could rusty coding skills and an unclear expectation of your job responsibilities.

With these pointers on how to prepare for a data science interview, you’ll be able to demonstrate expertise and commitment to your recruiters. 

So remember to get data science interview tips from past interview questions and brush up on your skills to fit the specific role you’re applying for.

Ultimately, the key to victory can be summed up by putting in the work to adequately prepare, which in turn gives you the confidence to succeed.