{"id":21811,"date":"2022-06-03T15:32:58","date_gmt":"2022-06-03T22:32:58","guid":{"rendered":"https:\/\/www.springboard.com\/blog\/?p=21811"},"modified":"2023-09-28T01:44:30","modified_gmt":"2023-09-28T08:44:30","slug":"data-science-interview-preparation","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/data-science-interview-preparation\/","title":{"rendered":"Data Science Interview Preparation: 7 Tips to Succeed"},"content":{"rendered":"\n

There\u2019s never been a better time to launch a career in data science. The number of data science jobs is estimated to grow by 30% this decade, and it\u2019s also one of the most lucrative tech roles, with median salaries for data scientists being around a hundred thousand dollars a year. <\/p>\n\n\n\n

But before you can start earning that six-figure salary, you\u2019re going to need to ace your data science interview. And this kind of interview is more than just a show of your technical skills. <\/p>\n\n\n\n

We\u2019ve put together this guide, so that you\u2019ll know what to expect from your data science interview, and how to prepare accordingly. Keep reading to learn how to ace the interview and land the job of your dreams. <\/p>\n\n\n\n

Are Data Science Interviews Hard?<\/h2>\n\n\n\n

Preparing for a data science interview is not unlike preparing for other job interviews. It\u2019s even more similar to interviews for other tech roles, so you won\u2019t be faced with anything that your fellow software developers haven\u2019t endured. <\/p>\n\n\n\n

Still, there are some idiosyncrasies. Let\u2019s take a look at what you should be ready to handle, and how to best respond. <\/p>\n\n\n\n

Data Science Interview Preparation: Where To Start<\/h2>\n\n\n\n
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Source: Dribbble<\/a><\/figcaption><\/figure>\n\n\n\n

Research the Role and Company<\/h3>\n\n\n\n

This is perhaps the most overlooked aspect of preparing for a data science<\/a> interview. Recruiters are looking to see that a candidate has taken the time to understand what the company does, and that candidates have a sense of how data scientists<\/a> can contribute. <\/p>\n\n\n\n

Start your research in the most obvious place: the company\u2019s website. It\u2019ll give you some basic information about what the company does and what its mission is. If it has an active blog, read through some of the more recent posts. This will also give you an idea of what the company is focused on, and where it\u2019s headed. <\/p>\n\n\n\n

Then, you can move on to some more in-depth research. Follow the company\u2019s LinkedIn page to get an idea of its social media presence. You should also connect with or follow some key decision-makers, as this will give you some insights into the company culture and what they expect from employees. <\/p>\n\n\n\n

Once you have a sense of the company, you should start researching your specific role. Thoroughly reading the job description is the first step, and you\u2019ll be surprised how much you can gather from the fine print. Noticing small but important details, and then mentioning these in the interview, will reassure recruiters that you\u2019ve done your homework. <\/p>\n\n\n\n

You can also follow and research people who already work in data science at the company you\u2019re applying to. Drop them a message on LinkedIn saying that you\u2019re applying for a job there, and that you\u2019d like to know how this job compares to other data science roles. <\/p>\n\n\n\n

Review Your Portfolio and Past Projects<\/h3>\n\n\n\n

While you won\u2019t get hired on your portfolio alone, your data science portfolio<\/a> is what will get you in the door for an interview. So spend as much time on your portfolio as you would on your resume<\/a>. And make sure that you tailor your portfolio to the company you\u2019re applying with, showcasing your most relevant projects. <\/p>\n\n\n\n

If you don\u2019t yet have anything to include in your portfolio, or want to expand it, then check out some of these data science projects<\/a> you can add. <\/p>\n\n\n\n

Another great way to build an impressive portfolio is by participating in competitions on websites like Kaggle<\/a>. In fact, there are instances where recruiters have reached out to data science candidates based on what they\u2019ve seen on their Kaggle profile. So make sure that you keep your Kaggle and Github profiles up to date. <\/p>\n\n\n

Get To Know Other\tData Science Students<\/h4>
\"Isabel<\/a>

Isabel Van Zijl<\/p>

Lead Data Analyst at Kinship<\/p><\/div>

<\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Esme<\/a>

Esme Gaisford<\/p>

Senior Quantitative Data Analyst at Pandora<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Garrick<\/a>

Garrick Chu<\/p>

Contract Data Engineer at Meta<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n

Brush Up on Foundational Concepts<\/h3>\n\n\n\n

To ace your data science interview<\/a>, you\u2019re going to have to demonstrate your technical aptitude. Whether you\u2019re just starting out, or applying for a senior-level position, technical recruiters want to see that you know your basics. <\/p>\n\n\n\n

Here are some of the technical concepts that you should review while preparing for your data scientist interview: <\/p>\n\n\n\n