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.
I know that just sounds like high demand and high supply, but what makes it chaotic is that often companies don’t quite know what exactly they want in a data scientist, and even if they do, they are not quite sure how to verify that candidates have those skills.
Candidates are presenting themselves with certificates, diplomas, and degrees from a bewildering array of education providers ranging from Ivy-league institutions to mom-and-pop style tutoring operations. They are also claiming to be competent in many programming languages, even though they are often stretching the definition of ‘competency’ in making those claims.
Having screened thousands of data science resumés, I can tell you that if you are out there looking for a new data science role, there are a few things you can do to make life easier for yourself. Doing these things will help you avoid being interviewed for positions that you’ll never get an offer for, but also help you make your skills more obvious for positions where you are qualified.
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.
So when I receive resumés where people just list five or ten different languages, making no differentiation between their skill levels, I’ll just assume that they know very little about all of them.
In all likelihood your potential employer wants you to be skilled in one language — either a specific one or any relevant language — so be honest about which languages you know well and are most fluent in. Try to list no more than two languages that you would describe yourself as ‘skilled’ in. Then if you want to list others that’s fine, but label them differently so that the reviewer can tell that you are not claiming to be skilled at everything.
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.
One way you can get past this is to actually provide examples of your work, by sharing links to applications you have built or by keeping a Github repository of publicly shareable code you have written. This allows reviewers to see whether the work you have done to date meets the requirements or expectations for entry-level of the role and goes a long way to reassuring them that your skills match your claims.
This of course means that you need to tend to your data science garden regularly. Get your code up onto github and keep it clean, tidy, commented well, and create easily navigable and well-understood repos. This will be an important skill to have for the rest of your data science life so it’s a great investment to make now.
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.
It’s also a good idea to do this because the data science community loves open-source activities and sharing of work. Some of your interviewers will be excited to see that you participate in this and it will score you extra kudos which might help them decide between you and another candidate. It’s a great habit to get into and to maintain even after you have been hired, as engagement with the community is the best way to learn new skills and stay up to date on developments.
So if you are wondering why your resumé never gets picked, there’s a good chance that it’s because you are not doing some of these things. I would highly recommend that you start to follow these tips going forward as they will help you to help yourself. Good luck — it’s crazy out there!