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3 Unintuitive Tips on Getting Hired for Data Science

Nearly a decade after it was called the sexiest job of the 21st century, data scientists are still in high demand. But breaking into the industry as a data scientist can still be a real challenge, particularly when you’re looking for your first job.

Part of the problem is that data science is still a relatively new field, and many companies don’t know what they need. Terms and titles used in job postings aren’t always consistent, and “data science” jobs may turn out to be something else entirely when you reach the interview stage and realize what the company actually needs is a data engineer.

But if you’ve got the skills to work in data science and aren’t getting interviews, part of the problem could be your approach. Here are three unintuitive tips that could help you get hired in data science:

1. Don’t focus on credentials

In other industries, having the right degree or certification is often a prerequisite for getting the job. In data science, however, that’s much less important.

While it’s certainly fine to list relevant credentials on a resume, one mistake some applicants make is emphasizing a data science certificate, for example, over the projects they’ve built.

Data science certification programs can be excellent, but their value is in the skills that they teach, not the bullet point that they add to your resume. Employers tend to ignore certificates because there’s no industry-accepted standard. Many certificate programs are also relatively easy for bad actors to “game”, with lax ID verification requirements and little in the way of meaningful testing or skills assessment.

What employers want to see is proof that you have the skills required to do the job. A certificate doesn’t prove anything, so unless you have prior professional experience in the field, your resume should emphasize the data science projects you’ve built that are most relevant to the job you’re applying for.

To be clear, having a Masters or PhD in a STEM-related field can help your chances of getting hired. But if you don’t have an advanced STEM degree, don’t worry — it’s definitely possible to get jobs in data science without them. What employers are concerned about first and foremost is skills.

2. Relevant projects are more important than education

If you’re trying to find your first job in data science, it’s worth keeping in mind a maxim that applies to almost any job: no one will pay you to do something you’ve never done before. That means you need to prove you have done the kind of work this job requires. And if you don’t have relevant prior work experience, that means demonstrating it with projects.

One mistake applicants often make is not tailoring the projects they highlight to the jobs they’re applying for. The closer your projects track with what you’d be expected to do at the company, the more likely you are to get hired.

That can mean some extra work up front, but it will produce much better results than listing the same four or five generic projects on every job application. You may also want to highlight the same project in different ways on your resume depending on the job you’re applying for, to highlight particular aspects of your analysis or certain technical skills that were mentioned in the job listing. In some cases, if there’s a job you really want, it may even be worth spending the time to create a project specifically for them.

Also: wherever possible, include a clickable Github link to projects. You likely won’t know what level of technical knowledge your resume reviewer has, but if they can click through and see an active Github with cool data projects, that never hurts!

3. Soft skills matter a lot

Data science is a technical field, but it is a field that also requires communication and creativity. In most data science positions, your job isn’t just to analyze data and build models, it’s also to communicate and interpret your results.

Often, your audience for this will be non-technical managers or executives, so your ability to write clean Python or optimize a machine learning model is not the only thing that matters. A great data scientist is also a communicator who uses data to tell a story.

That’s a skill that your resume and portfolio projects need to demonstrate. How are your projects presented? Is your data visualized in clear, attractive, understandable charts and graphs? Have you laid out a narrative that puts your analysis into a business context, or is your Github simply a wall of code?

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Summary:

Even in data science, job applications are a bit of a numbers game, and there’s no perfect approach that’s going to get you hired at 100% of the jobs you apply for. But these tips — which are based on interviews with recruiters, hiring managers, and data scientists — can definitely help you improve your chances of success.


About the author: Charlie Custer is a content marketer and marketing analyst at Dataquest with experience from everything from After Effects animation to Python programming. He spends his spare time mountain biking with his wife and pretending to be a dinosaur with his daughter.

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