Why is your Data Science resumé not getting you interviews?

 Data science recruiting is just crazy right now. So many companies and organizations want to hire data scientists, and so many people are coming onto the job market claiming to have data science skills.

1. Don’t claim to be skilled in more than two languages, because in all likelihood that is not true

To be truly skilled at a programming language takes time and investment. It takes years to be fully confident in one language. Developing confidence in a second language adds further time and commitment. Being able to fluently move between the languages also requires work.

2. Link to examples of your code to prove your skills

One of the biggest gaps in data science recruiting currently is the difference between what people say they can do and what they can actually do. Reviewers and interviewers can get cynical about people’s claims very quickly because of the amount of time they have wasted interviewing people whose claims just did not stack up against reality.

3. Get involved in public/open-source coding work to build a portfolio

If all the work you do is proprietary, that creates a problem for you if you have to demonstrate your work to others in job applications. Getting involved in some open-source work, like helping to develop Python or R packages, or writing and publishing your own learning or analysis projects, will help you build up your portfolio of sharable work.

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