Cracking the Current Data Science Job Market: Proven Strategies from a Tech Data Scientist
I graduated in the U.S. amidst the global pandemic of 2020 when the job market was a hostile land. Now I’m happily working as a Data Scientist at Spotify, but getting there was a long ride.
Navigating the Data Science job market is like fighting on a battlefield with lions and kittens. Lions can kill you and kittens will most likely hinder you too. You’ll understand later what I mean by this.
I remember applying to hundreds of positions and never getting a single call back for an interview. It made me doubt my abilities but in hindsight, most of the reasons why I was failing were beyond my control.
If you’re going through something similar, then maybe this post can help you find the best approach to navigating the current job landscape.
In recent months, I’ve had multiple discussions with both experienced Data Scientists in Tech and aspiring ones about the current state of the data science hiring market.
I’ve come to understand:
- Why it’s become challenging to get hired right now
- How to maximize chances of getting a job
And I will share these with you in this article.
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The Job Formula
With the current economic landscape, I’ve been increasingly hearing about the struggle many Data Scientists face in securing a job. Whether they be fresh graduates from college or laid-off experts from MAANG, everybody is struggling at the moment.
If you’re in this situation, know that your inability to get a job is a multilayered problem. If you want to better redirect your efforts to put all chances on your side, understanding why this is happening is crucial.
Chances of Getting an Offer = (Skills + Experience) * Market Conditions
Getting a job depends on your skills, past experience, and the most important one: the conditions of the market, something absolutely beyond your control.
Currently, the job market isn’t the most welcoming of new joiners. We all know the economic landscape isn’t helping, but what are the downstream effects? How is this related to your struggles?
The Aftermath of the Economic Downturn
I have witnessed so many Data Scientists, some of which I personally knew, lose their job in the midst of the layoff wave that’s been sweeping away the Tech market worldwide.
There are 3 downstream effects of these massive layoffs that are most likely affecting your ability to secure a job at the moment, even if it’s not in Tech:
- Oversupply of data experts
- Low headcount in companies
- Company maturity & scale
1. Saturated Market
There has been an oversupply of people applying for data science jobs over the years. It seems like everybody bit the hook when data science was dubbed the hottest job of the century.
This also means that over time, the data science market has been increasingly saturated with new joiners.
There is an oversupply from:
- Self-taught enthusiasts riding the wave
- Fresh graduates from data science programs
- People with technical backgrounds transitioning to data science, including PhDs (which are usually very hot in Tech)
- People coming out of boot camps or holding data certificates
This means that when you apply for a position, you’re competing with these people too, the experienced lions and the less qualified (or unqualified) kittens. This has been the case for some time now.
But what makes the game even harder to play is that now you got yourself a wave of freshly laid-off experts joining the party.
That’s how you end up playing Hell difficulty level in the game, even though you asked for nothing (except for a job).
This also means that your chances of getting rejected are higher than the norm for two reasons:
1. You’re competing with laid-off sharks from Tech.They have a solid experience and they might ultimately outshine you in the process. Even if you’re one of them, you’re still competing against them.
- If the company is large, these people might have more chances of getting hired, and the barrier to getting a call back from a recruiter for someone with less experience will be larger.
- Companies of smaller scale might not be able to afford these experts because of the high salary & benefits expectations they have. If you have low experience, then aiming for these firms could increase your odds.
2. Most companies don’t have sophisticated hiring systemsSome of them end up cutting brilliant heads through the filtering process because they lack the means to filter out such a massive inflow of applications. This means that your head might end up being on the spike sometimes.
Standing out of the crowd becomes your new goal, but we’ll talk more about this later.
2. Low Headcount
The second downstream effect of this economic splash is that now, companies are more reluctant to hire people. Because companies now have different priorities (Profitability > Growth), this means:
- Hiring budgets get sacked in the process, so headcount is limited. Companies are hiring only based on their needs, so they will most likely be very picky in their hiring process.
- Candidates with high experience are playing in the front rows as companies can get more ROI from them than more junior ones.
This means that you’re competing with more people for a smaller number of positions.
When I was applying for jobs in 2020, companies were laying off people for the same reason: saving costs.
The job market is unfortunately highly volatile, but this is also good news because it means things can shift back quickly too.
3. Company Scale
Not a lot of companies are at the stage where they need a Data Scientist. We mostly enter the game when companies have gathered enough data over time or when data is the product.
For a lot of Tech companies, take Netflix for instance, data is the product.
They derive most of their value from analyzing their data and making products out of it with Machine Learning.
Can you imagine Netflix running without their systems recommending you entertainment catered to your taste? Data is business crucial here, and so are data scientists.
Companies that do not rely on data to grow and be profitable will most likely think twice before hiring data scientists in the midst of the recession. Most data scientists become luxury commodities. Not everyone can afford us.
What can you do?
Now you need to tweak the rules a little if you want to be in the game.
Because the landscape is fiercely competitive, your job is to do your best to stand out from the crowd. Some of the things I did that helped me and other data scientists level up in the game are:
- Personal Branding
- Networking the Right Way
- Specializing
- Applying Abroad
1. Personal Branding
Make sure your LinkedIn is neat. Anything that can help you look like a treat should be in there.
Next, your resume. If you have little to no experience, then you need to bring your projects to the front row. If you haven’t done many data science projects, then it’s no surprise you’re not getting a call back. Go back to studying!
Some things you can do:
-
Curate your resume. Of course, you won’t be able to adapt your CV to each company you apply to. I once applied to a position that required some knowledge of clustering techniques, so I made sure to highlight the projects where I did clustering. So what you should do is:
- Pick 3–5 positions you’re really interested in, for which you also qualify.
- Research the skills of the current employees from this company that hold similar roles, and identify which skills you share with them, that are also on the job application.
- Highlight those specific skills on your resume + anything else from your profile that aligns with company values.
- Create + optimize your portfolio. It can be a website or on GitHub. What matters is for you to showcase what you can do. Make sure to include a link to your portfolio on your resume!
- Highlight projects where business acumen was put to use. Most Tech companies hire data scientists to help them further their business goals. So you need to show that you’re able to make sense of data in a business context.
One of the main reasons I was hired at Spotify (or so I was told at least) was because I had a business background mixed with a data science skillset. The best data scientists are the ones able to reconcile both, so show that you can (if you can)!
As a matter of fact, I’ve crafted a step-by-step article to guide you in designing a winning resume for data science jobs. I dissect the same resume that got me my job at Spotify in this article. Don’t miss it!
2. Network the Right Way
Leverage your network to get a foot in the door. I got into Spotify by applying through the website, the traditional way. But if you want to increase your odds of getting a reply, then I’d suggest putting your network to work. That’s how I got an internship offer at Ubisoft, even after getting rejected at the same stage.
This doesn’t mean reaching out to random people and asking them for referrals.
When someone refers you to a company, they commit that they’re referring someone they trust, someone they know can contribute positively. Random people don’t know you so they are most likely not even going to reply to your referral requests.
This means that the best thing you can do is try connecting with people already in your network.
Ultimately, alumni from the college you attended, hiring managers, previous colleagues, even friends, or friends of friends, but not your friend’s dog.
What matters is to have some kind of sound connection with the person that you can leverage.
This does not mean that they will refer you, but at least you might be given the chance to prove your abilities to them. This doesn’t mean that you will get the job either, but at least it might put your resume in the spotlight instead of getting filtered out without being given a chance.
I’ve written a whole article on how networking got me a top opportunity in Tech and the 6 steps you need to follow to stand out in the crowded data job market. Be sure to check it out!
3. Specialize!
AI has been booming everywhere, and with it, the market for data jobs grew so much recently, it ended up splitting into different branches:
- Data Specialists: Data Scientists, Data Analysts, Data Engineers, Machine Learning Engineers, Research Scientists
- AI Specialists: NLP Scientists, Cloud Engineers, Computer Vision Scientists, LLM Scientists, Stable Diffusion Specialists etc…
and the maniac only grows by the day. Catching up with the wave is tough, but knowing which one to ride could save you from drowning.
Talking to everyone is talking to no one
If your skillset has become way too broad for the data market, then working on narrowing down your skillset to a specific niche could increase your odds. This way, you might end up becoming more attractive while competing with much fewer people.
4. Apply outside the U.S
There is life for data scientists outside of the U.S. I’m saying this because a lot of Tech companies are headquartered there so a big chunk of the people struggling in the job market are also based there. I know because I was one of them too.
If hiring slowed down in the U.S., know that it hasn’t completely frozen in the rest of the world.
In countries like France where I live, quality data scientists do not grow on trees, so if you happen to be unchained to a specific country, then I’d suggest broadening your horizons to other lands.
Because the data science job market is so competitive in the U.S., moving to France proved to be a strategic decision in helping me secure my dream job at Spotify.
In France, there are currently almost 20k job openings in data science alone (cf. picture below), and I’m sure this is the case in many other countries too.
Of course, I understand some people just don’t have the luxury of moving abroad for many reasons. But we live in a post-covid world where working remotely is increasingly becoming the norm.
One thing you can do if you cannot move abroad is to still apply to another country, whether you’re living in America or elsewhere.
I live in Paris, but most of my team is scattered across Europe. Distributed work is increasingly enabling talented and hard-working people to find opportunities beyond their borders. What about giving it a shot?
Recap
Why is it so hard to get a job in data today?
The economic downturn has led to 3 downstream effects on the job market:
- Oversaturation of the data job market with applicants
- Limited hiring due to companies focusing on profitability over growth, resulting in more competition for fewer positions.
- Companies that do not heavily rely on data for their business may be hesitant to hire data scientists during the recession.
What can you do about it?
- Personal Branding. Ensure your profile is well-presented and optimize your portfolio with relevant projects that showcase your business skills.
- Network the Right Way. Leverage your network to improve your odds of getting noticed.
- Specialize. Narrow down your skillset to a specific niche to stand out.
- Apply Outside the U.S. Consider opportunities (including remote work) in other countries with growing demand for data scientists.
Of course, these are not miracle solutions. The goal is to help you optimize your approach to Job Hunting to increase your chances. So may the odds be in your favor!
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