How to Write a Great Data Science Resume

How to Write a Great Data Science Resume

Keep Data Science Resumes Brief. The first thing you should strive for in writing a resume is to keep it short. Customize Each Resume to the Job Description and Company. Choose a Template.

Writing a resume for data science job applications is rarely a fun task, but it is a necessary evil. The majority of companies require a resume in order to apply to any of their open jobs, and a resume is often the first layer of the process in getting past the “Gatekeeper” — the recruiter or hiring manager.

A resume ( résumé, CV), by definition, is a brief written account of your personal, educational, and professional qualifications and experience.

Writing a brief summary of your own experiences sounds like an easy task, but many struggle with it. Here are some tips about how to write a clear and concise resume that will catch the eye of a recruiter/hiring manager.

(This article is a part of our in-depth Data Science Career Guide. To read the other articles, please refer to the _[table of contents](https://dataquest.io/blog/data-science-career-guide) or the links that follow this post.)_

Keep Data Science Resumes Brief

The first thing you should strive for in writing a resume is to keep it short. A good resume should only be one page long, unless you have ten years of relevant experience for the job you’re applying to.

Even then, there are recruiters out there who will toss any resume longer than one page. Recruiters/hiring managers receive a LOT of resumes every day, and they usually have about 30 seconds to look over someone’s resume and make a decision.

“Let me be honest,” says Stephen Yu, president and chief consultant at data analytics consulting firm Willow Data Strategy. “Before I meet somebody, the time that I spend [on each resume] is less than 30 seconds. If that resume doesn’t speak to me, which only happens with one in ten resumes anyway, I’m not even going to call the candidate.”

So, although you might have dozens of data science projects you'd like to highlight, you need to prioritize. You want to boil your experience down to the most important, relevant points so it is easy to scan.

data-science

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