It’s easy to find work in data science and analytics – provided you have the right skills. While the pandemic certainly slowed things down, the demand for data skills remains strong. People with professional experience in SQL, Python, and R will likely find themselves hearing from recruiters more and more – much like they did before the pandemic.
When work naturally finds its way towards you, you might find yourself asking – does networking really matter in data science?
Yes. 100% yes. There's two big reasons why.
Reason #1: It’s Easier to Break Into Data Science with Networking
As you probably noticed, data scientist positions often require two or five years of professional experience. That’s regardless of your education or skill set.
That makes it hard to land a data scientist job without already being one. It’s that famous catch-22 – how can I gain the experience if all the jobs require it?
You might decide to apply anyways and see if you get lucky, but so do a hundred other people. That makes it difficult to stand out among these other applicants with the same skill level as you.
Networking – done well – helps you circumvent these barriers.
Reason #2: It’s Easier to Control Your Career Direction with Networking
It’s not uncommon in any career to find yourself carried in directions you don’t want. A boss will tell you to focus on these projects, which develops you in a skill set you care little for. Before you know it, the only job offers you receive are for work that doesn't interest you.
This happens a lot in data science. I’ve met many aspiring data scientists who found themselves in unrelated roles. Roles, such as database development, report development, web tracking, etc.
Networking, though, allows you to better define the career you want and gain allies who will help you get there.
So Networking Is Important... How Do I Do It?
I wrote a long article here about my networking strategies. I suggest reading it for specific, real-world advice on networking.
To summarize my advice though, I suggest treating networking as “relationship building.” It’s not about taking someone out to lunch and asking them for a job. It’s about cultivating friendships in a career field you’re passionate about.
How do you start building those relationships?
Here’s a few quick ways:
Attend data science user groups and meetups, such as R User Group, Python User Group, Data Science Meetups, etc.
Reach out to people and speakers you meet at those groups for lunch or coffee; find a way to make these conversations fun and educational and less-so about getting a job
Continue speaking and meeting with those people that you enjoyed talking with
Eventually, those people will start recommending you for jobs where they work or to other interested employers.
There’s more to networking than that, but that’s the quick summary. It’s kind of like dating! You keep meeting new people until you find “the one.”