By Frederik Bussler, Growth Marketing at Apteo.
Getting a data science job is as easy as learning skills like Python and Jupyter, competing in Kaggle competitions, getting certified, and submitting your CV on job portals, right?
Recently, Kaggle hit 5 million unique users. Looking at other communities, Towards Data Science gets 20 million monthly views. AI researcher Andrew Ng has 4 million learners across his Coursera courses.
In the meantime, there are just 17,100 ML Engineers worldwide. As of writing this article, there are 2,100 open Machine Learning Engineer roles on LinkedIn, worldwide. Of those, around 80 are at the FAANGs.
“Open Data Science Roles” by Apteo’s “data science career trends” dashboard.
To be fair, if we include general data science and AI researcher roles, there are 86,000 total open positions worldwide. Nonetheless, calling data science “competitive” would be the understatement of the year.
Data science communities vs. data science job opportunities. Visualized by author.
Red Ocean vs. Blue Ocean Strategy
In business, a competitive field is referred to as a “red ocean,” where sharks are battling it out in a crowded space. In contrast, a “blue ocean” refers to unexplored, competitor-free market spaces.
As a prospective data scientist, your goal is to compete in the blue ocean. Here are four ways to do so.
Red Ocean vs. Blue Ocean strategies. Created by author.
1. Use Niche Platforms and Communities
I’m a LinkedIn superfan. In fact, I’ve been featured by LinkedIn after taking 100+ LinkedIn learning courses, I’ve made posts with over 10,000 comments, and I have over 20,000 connections. That being said, relying solely on LinkedIn is a red ocean strategy (⚠️).
LinkedIn post, by LinkedIn.
LinkedIn boasts around two-thirds of a billion users. That means if you solely network on LinkedIn, and apply for jobs through LinkedIn, you’re competing with an incredible amount of people.
As someone who has used LinkedIn to hire people, good job postings can get flooded with so many applicants, so quickly, that it’s hard to even skim through all the applications, let alone give feedback to everyone.
That’s why, in addition to LinkedIn, I’d recommend that you also use niche platforms, like Shapr, Y Combinator’s Work At A Startup, Lunchclub.AI (note: this is my invite link, but I don’t get paid), Slack communities like Wizards, as well as offline communities on Meetup or Eventbrite.
All of these are free.
Tight-knit communities make it far easier to stand out. Shapr and Lunchclub, for instance, are professional networks completely focused on creating one-on-one connections.
2. Network to Avoid the “Black Hole Effect”
I once met a conference-goer who joked that “networking is the new way of saying not-working.” As amusing as that was, I also met a new client at that event.
Networking is similar to the first point, but here I’m referring explicitly to when it’s time to apply for a job.
You’ve probably heard stories of people submitting hundreds of job applications and not getting a peep back. Maybe you’ve been a victim of that phenomenon yourself.
While some people do land jobs this way, it’s increasingly likely that submitting your CV on some job portal means you’ll never get a reply back. As I mentioned, hiring managers are getting absolutely flooded with applicants.
It’s like tossing your CV in a black hole.
Ask yourself: If you had to decide who to hire, between a complete stranger, and someone whom you were introduced to, who would you pick?
As a result, hiring managers and executives almost always go with the applicant that they have some connection with, even if it’s just an introduction by a mutual acquaintance.
The more you network, the more mutual connections you’ll have with potential employers, and the easier it’ll be to get an introduction.
LinkedIn “mutual connections.” By author.
Here’s a super simple template you could use to ask a mutual connection for an intro:
First, engage with their latest posts, then message.
I hope you’re doing well. Since we’re in the same industry and have a mutual connection with [@name] at [@company], I was hoping you could introduce me over LinkedIn. I recently applied for their [job posting title].
I wrote a draft message you can copy/paste to them for an easy intro:
I noticed you’re hiring for a [job posting title].
I’d like to introduce you to [@ Frederik Bussler] as a potential candidate, who has [achievement_1] and [achievement_2] in the space. Frederik’s interested in speaking with you about the position. Would you like a quick intro?
Data Science Venn Diagram, created by author.
Data science is a multidisciplinary field, and a large component is domain expertise.
For example, Walmart uses predictive models to anticipate demand at certain hours. If a hiring manager for a data scientist role had to choose between a “Python expert” and an “expert in predictive modeling for retail,” obviously the more specialized candidate would win, all else being equal.
Amazon’s recommendation engine is responsible for up to 35% of Amazon’s revenue, and they’re constantly hiring data science talent to grow this golden goose. If you’ve worked on recommendation engines — even just as a side project — that’d give you an edge over a more generalist applicant.
Learning specialized skills — which speciality depends on your personal interests — is a game-changer.
4. Practical Projects > Certificates
Certificates are all the rage in 2020. If you use LinkedIn, you’ve surely noticed the infamous “certification post.”
Users show off their certificates from Coursera, EdX, online-learning portals, LinkedIn Learning, and a million other sources. I’m guilty of this as well — as I mentioned, I’ve taken 100+ LinkedIn Learning courses, and received certificates from almost every Ivy League.
That being said, relying on certificates is a red ocean strategy (⚠️). When millions of people have the same certificates as you, then you need differentiators.
Comparing data science jobs and Andrew Ng’s Coursera students. Created by author.
Practical projects, where you analyze data that you’re interested in, will give you a massive edge.
Data Science is an increasingly competitive field, but you can stand out by using niche platforms, growing your professional network, specializing in an area that interests you, and sharing unique projects with the world.
Original. Reposted with permission.