4 of the fastest ways not to get hired as a data scientist

I’ve read a lot of data science resumes working at SharpestMinds. Because the platform is built to be a giant feedback machine, we constantly hear back from companies not only about whether they want to interview or hire candidates, but also about why they chose to interview or hire the ones they did.

That visibility into the decision-making processes of hundreds of companies has taught us a lot about what good resumes look like and, just as importantly, what they don’t look like.

Caveat: Every company looks for something different. What gets you hired at Google may or may not work at other companies (and may even be meaningless). So, building the “perfect,” all-purpose data science resume is all but impossible.

Having said that, there are a number of mistakes that guarantee that your application won’t be considered:

1. Featuring trivial projects on your resume

It’s hard to think of a faster way to have your resume thrown into the “definite no” pile than featuring work you did on trivial proof-of-concept datasets among your highlighted personal projects.

Here are some projects that hurt more than help you:

• Survival classification on the Titanic dataset
• Handwritten digit classification on the MNIST dataset
• Flower species classification using the iris dataset

Why it hurts you

Space on a resume is limited. Candidates and recruiters know it. So, if “training wheel datasets” like MNIST are taking up some of that precious space, it can raise questions in recruiters’ minds about how far along you could really be in your data science journey.

What to do about it

If your resume features these kinds of projects—and if you don’t have other, more challenging and substantial ones to replace them with—it’s a strong indication that you need to put some serious time into building up your portfolio.

Of course, if you do have other, more interesting projects to showcase, then you’ll definitely want to swap them in.

Exceptions

2. Listing Udacity or Coursera projects in your portfolio

3. Absence of version control/devops/database skills

4. Not having learned anything from the projects you’ve built

Bonus: Typos