Data science is a hot field, but some job seekers are having a hard time getting a foot in the door. Job posters are citing mismatches in skills needed on the job and the skills potential job seekers have. If you play your cards right, you could turn this skills gap to your advantage. If you ignore it, you could be one of the many data scientists perpetually stuck in the loop of job seeking, networking, and getting nothing back. Here’s how you can ensure you’ll get passed over in data science every time.
Stick with What You Know
You learned one language in the beginning (was it Python?) and you’ve coasted on that one. Learning a language is an accomplishment you should rightfully be proud of, but if you haven’t figured out what your dream employers are looking for, you could be missing out.
Researching your chosen niche or organization to find out what languages and frameworks they employ in their data science departments is crucial to getting your foot in the door. It won’t matter the complexity of your algorithms if they aren’t a good fit for your organization’s current ecosystem.
Organizations aren’t just looking for hard data science skills. They’re also looking for business understanding and even more importantly, soft skills necessary to connect data science to business. You’ll need to brush up on your communication skills and consider getting an understanding of the business side of things. Sit down with a mentor who’s been working in business or offer to shadow a connection who can introduce you to the business world.
Don’t do anything beyond the basics you learned in your boot camp or MOOC, and you’ll be destined to job seek forever.
Focus on Theory Only
Data science is cool for data science’s sake, but if you’re only focused on the most sophisticated algorithm around, you aren’t going to connect with potential employers. When you begin learning data science, you’ll want to conquer all the cool things (as you should), but at some point, you’ll have to pivot to real-world value.
In most jobs, data science doesn’t exist in the cold, perfect world of theory. Data is messy. Timelines are shortened. Algorithms that are good enough to get results simply and quickly are prioritized. All the things you did to get recognized in an academic setting won’t fly in the business world.
You have to learn how to balance your love of pure data science with the often messy data of business. Once you understand the best ways to extrapolate insight, present that insight, and work with enough data to get through with the project without compromising the quality, you’re much better off.
Don’t Bother with Real Experience
You don’t want to work for free, but there are ways to get experience in real-world settings. You can sit around and apply to job after job, or you can go out to network and find ways to apply your skills in a new setting.
Consider participating in Hackathons and open projects. Hackathons allow you to tackle a problem head-on with your peers and develop a program or solution to a pressing issue. Another option could be applying to organizations like Omdena, which builds networks of data scientists and AI engineers can collaborate on projects to help solve real-world problems. These types of experiences can help make you more approachable and sell your skills to potential employers.
Without some real experience, companies may not take the chance on your resume. Data science has changed the job hiring process. While you don’t necessarily have to have a certain number of years of experience, companies like to see that you’ve participated in or created a solution for a real-world problem set.
The Best Way Not To Get Started in Data Science
The worst way to get started in data science is never to try. Networking through real connections, building experience in unique ways, and learning about the business are all excellent ways to connect your skills to the job world. However, if you don’t take the plunge in the first place to learn your data science skills or you never leave your day job for an exciting field, you’ll be guaranteed never to get your foot in the door. Just get started in data science and let the passion for data ignite.