6 Do’s and Don’ts for a Data Science Resume that Gets You Hired

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Data scientists may be in high demand, but no matter how talented you are, you won’t get hired without a high-impact resume. We’ve rounded up some can’t-miss tips for effectively showcasing your value to a potential employer, and we’re going well beyond the basics. Of course, general best practices still apply: the age-old rule of a one-page max is still in effect (and If Marissa Mayer can do it, so can you).

Read on for the essential do’s and don’ts of creating a resume that doesn’t just get you noticed –– it gets you hired.

DO: Emphasize metrics to showcase success

If your resume is full of ambiguous accomplishments, your chances of getting a call to interview are slim to none. Employers want to see that you can deliver hard numbers, not empty promises.

Do Don’t
Grew a team of 4 to a team of 7 with no attrition and 30% revenue growth year-over-year Grew a disorganized team into a great one
Reduced model error by 20% and reduced training time by 50% Achieved superior model performance

DON’T: Bury your skills in fluff  

Michael Li, founder of The Data Incubator, sees tens of thousands of resumes. He advises applicants to “avoid weasel words –– words that create an impression but can allow their author to ‘weasel’ out of any specific meaning if challenged.” Li uses “talented coder” as an example: this may sounds like the ideal description, but it’s entirely subjective. Who can verify this statement aside from the person making it? “Contributed 2,000 lines to Apache Spark” can be verified on GitHub, making it a much stronger claim.

Do Don’t
Statistics Ph.D. from Princeton and top thesis prize from the American Statistical Association Strong statistical background
Reduced model error by 20% and reduced training time by 50% Achieved superior model performance

The latter can be verified, and doesn’t rely on a subjective self-assessment.

DO: Customize your resume for every employer

Sending out the same resume to every job you apply to may be efficient, but it’s not effective. Every job requires different skills, and your resume should be tailored to reflect these demands. Refer to the job listing to determine what experience to emphasize –– you can even comb Linkedin to see what current employees are endorsed for most frequently. Whatever you do, don’t depend on the reader to figure out why you’re a perfect match for a role.

DON’T give up just because you don’t have on-the-job experience

Will Stanton, a data scientist with Return Path, has great tips for creating a resume that proves you can do the job, even without previous working experience. Stanton says there are “Three main ways to do this: independent projects, education and competence triggers.”

  • Independent projects can come in the form of Kaggle competitions, a Github repository and more. Check out Stanton’s advice on projects here here
  • Education can also help prove your ability; If you have a masters or PhD in a relevant field, make sure to include it, along with any related coursework
  • Competence triggers are traits or accomplishments that signal to your target audience that you are “one of the club”, even without similar career experience. Stanton lists a few key competence triggers that will boost your resume: a Github page, Kaggle profile, a StackExchange or Quora profile and technical blog

DO: Highlight business acumen along with your DS skills

Simply listing all the technologies or programming languages that you’ve worked with won’t distinguish you from hordes of other candidates with the same skillset. Business savvy is increasingly important in the data science field, and shouldn’t be missing from your resume. From intellectual curiosity and business acumen to communication skills, you need to include that demonstrate that you understand any industry-related issues you need to solve, you’re able to work well with teams and clearly translate technical findings to non-technical departments, etc. Need a little more detail? Check out this post by Will Stanton.

DO: Talk about data size

In this field, size matters. Employers want to see that you have experience with large data sets, and they may also need a little help understanding the technical challenges you overcame during analysis. Michael Li suggests that applicants consider framing their work as follows:

Reduced model error by 20% and reduced training time by 50% by using a warm-start regularized regression in scikit-learn streaming over 2TB of data

If all you do is list data size, your potential employer may not fully grasp how much hard work went into your accomplishments.

Congrats! You made it to the end and hopefully created a killer resume in the process. Maybe not as awesome as this guy’s version of a CVS, but it will go a long way towards helping you land the job you want.

Blogs referenced:
Will Stanton’s Data Science Blog
Michael Li’s post for O’Reilly Data

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