data science was dying 7 months ago?
It was also dying 2 years ago.
And dying 3 years ago.
And never to say it was also dying 5 years ago.
Nonetheless, from where I stand, this is unquestionably not the case. People still appear to land data scientist jobs.
I mean, I literally help people do that every week in my coaching programme.
So, what on earth is occurring?
Well, in this text, I need to interrupt down:
Let’s get into it!
Market Outlook
As a lot of you’ll know, there have been significant layoffs during 2022 and 2023, with nearly 90,000 tech employees being laid off in January 2023 alone.
The truth is, it was so severe that TechCrunch even created an archive of all of the layoffs that occurred during this era!
Nonetheless, in response to a study by 365datascience, data jobs weren’t that affected by these layoffs; they found that:
For instance, only 2.7% of individuals laid off from Amazon during this era had the title of knowledge scientist.
In keeping with one other study:
And we may also see that the salary of knowledge jobs as a complete has been growing through the years.
So, it’s clear that data science is just not dying in any respect; if anything, it’s growing.
Nonetheless, why does it feel very hard to get a knowledge scientist job at once, especially on the entry and junior levels?
To elucidate that, we want to look past the numbers and really understand what the trendy data scientist is.
Data Science Evolution
As an insider on this field, let me inform you a secret.
Data science is just not dying; it’s evolving.
10 years ago, corporations would hire data scientists to tinker with machine learning models in Jupyter Notebooks.
The truth is, this is strictly what my first data science job was like.
A knowledge scientist was like a Swiss Army Knife — one person expected to do every thing from cleansing data to constructing models and presenting to the CEO.
Nonetheless, over time, corporations realised they were getting no return on investment from this strategy, in order that they became more stringent about roles and responsibilities to make sure they weren’t wasting their money.
This has led the info science job to turn out to be fragmented, and the title has turn out to be meaningless, as you can find data scientists doing completely different jobs at different corporations.
Usually, three flavours of knowledge scientists exist today.
Analyst
The sort of data scientist is closely aligned with the business side and primarily focuses on reporting workflows and experimentation.
For instance, you’ll:
- Get data from an organization database or other sources.
- Write some code that may be very linear and bespoke by nature, starting with ingesting data, cleansing it a bit, then doing a little EDA and a few inferential or basic modelling work.
- Once complete, you place together a report that details the evaluation, provides visualisations and other metrics, and offers a advice based on the evaluation’s goals.
The sort of data scientist is more of a knowledge analyst and typically requires more business domain knowledge.
Engineering
The main target of this sort of data scientist is on constructing and deploying solutions. This could be a range of things like:
- Internal software tooling
- Machine learning models that drive decision making
- Constructing libraries
This role leans more toward software engineering, but unlike a software engineer, it requires greater knowledge of maths, machine learning, and statistics.
Nowadays, this sort of job has moved beyond the “data scientist” title and is now called a machine learning engineer.
This is just not entry level position, and normally requires 2–3 years experience in an adjoining role like a software engineer or analyst first. So many graduates and other people with little experience would struggle to interrupt into this specific data science position.
Infrastructure
The sort of data scientist is the rarest, mainly since it has its own title: data engineer.
The goal of this role is to construct the info infrastructure and pipelines to accommodate the business’s data. This data is then used downstream by machine learning engineers, analysts and even non-technical stakeholders.
This role has turn out to be increasingly essential, especially with the emergence of generative AI in recent times, which requires the power to effectively store large amounts of knowledge and stream it with low latency.
At some corporations, chances are you’ll even be an analytics engineer, which is a more business-focused data engineer.
Junior vs Senior
A study published in September 2025 has been making quite a number of waves in the info and machine learning space.
The study examined 285,000 corporations between 2015 and 2025 and the way their adoption of GenAI has affected their hiring processes for junior and senior positions.
You may see within the plot below that hiring for senior positions remains to be increasing, whereas hiring for junior positions is decreasing.
This makes intuitive sense, as juniors’ responsibilities are likely easier to automate with AI than seniors’ as a result of the wealth of experience they’ve built through the years.
What I intend to make clear, though, is that corporations aren’t making juniors redundant nor are there no more junior positions left in the marketplace.
Most individuals will have a look at this graph and think that the junior data science market is becoming extinct. But that’s objectively not the case.
Hiring remains to be happening, however the rate of latest positions being posted is just not increasing. The availability curve stays unchanged while demand stays high.
What Can You Do?
I’m going to be honest, it’s becoming more competitive to interrupt into data science, nevertheless it’s not unattainable.
Gone are the times when all you needed was basic Python and SQL, and having done Andrew Ng’s Machine Learning course.
These are things everyone has nowadays, so you might want to go the additional mile and differentiate yourself greater than you used to.
There are numerous ways of doing this, for instance, you adopt and specialize in certain technical domains like:
- GenAI
- Model deployment
- Time series forecasting
- Suggestion systems
- Domain-specific expertise
Specialists are arguably becoming more essential as knowledge is increasingly democratised by AI. Having deep expertise is sort of a rarity nowadays.
An alternative choice is to go for a lower-level position, like a business or data analyst role, that’s more friendly to junior and entry-level positions, after which slowly construct your way as much as a full-time data scientist position.
It’s best to also give attention to areas that AI can’t really replace:
- Communicating effectively with different audiences
- Understanding the business impact of your work
- Critical considering and knowing what problem to unravel
- Strong fundamentals in maths and statistics
- Relationships and network
These are timeless skills, especially the last one.
You would possibly have heard the saying:
I actually disagree with this.
The actual power is in .
If you will have a solid network and relationship with many individuals in the sector who value and trust you, you possibly can tap into this to get referrals, opportunities, and even expand your network further.
The leverage this provides is incredible. I at all times tell my coaching clients that referrals and networks are actually the golden ticket to getting top-end data science jobs.
And all it requires, is just effort and pushing yourself out of your comfort zone to talk to people you need to connect with.
Technologies will come and go, but actual human relationships will remain central on your whole profession.
The reality is, you will must reinvent yourself every 3–5 years as a knowledge scientist, since technology shifts in a short time.
So asking misses the purpose.
Data science is at all times technically dying because it’s consistently evolving and remodeling.
But that’s what makes it exciting.
And in case you are willing to up-skill and put in additional effort than others, you shall be rewarded thoroughly.
In case you’re able to dive into data science after reading this, that’s an incredible first step.
But here’s the truth: I’ve been on this field for five years, and searching back, I spent my entire first 12 months on tasks that were an entire waste of time. In today’s hyper-competitive market, you don’t have the posh of trial and error.
To avoid my mistakes and speed-run your progress, take a look at this guide where I map out exactly how I’d turn out to be a knowledge scientist again.
One other Thing!
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Dishing The Data
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