Is the AI and Knowledge Job Market Lifeless?

knowledge science was dying 7 months in the past?

It was additionally dying 2 years in the past. 

And dying 3 years in the past.

And to not point out it was additionally dying 5 years in the past.

Nonetheless, from the place I stand, that is undoubtedly not the case. Folks nonetheless appear to land knowledge scientist jobs.

I imply, I actually assist folks do that each week in my teaching programme.

So, what on earth is occurring?

Properly, on this article, I need to break down:

• What the present knowledge market appears to be like like
• What it really means to be a knowledge scientist
• And, what you need to be doing to land a job on this present local weather

Let’s get into it!

Market Outlook

As a lot of you’ll know, there have been vital layoffs throughout 2022 and 2023, with almost 90,000 tech workers being laid off in January 2023 alone.

In truth, it was so extreme that TechCrunch even created an archive of all of the layoffs that occurred throughout this era!

Nonetheless, in response to a research by 365datascience, knowledge jobs weren’t that affected by these layoffs; they discovered that:

Apparently, our pattern’s largest group of laid-off workers didn’t maintain tech jobs — 27.8% labored in HR & Expertise Sourcing, whereas software program engineers got here in second with 22.1%. Advertising workers adopted them with 7.1%, customer support with 4.6%, PR, communications & technique with 4.4%, and so on.

For instance, solely 2.7% of individuals laid off from Amazon throughout this era had the title of information scientist.

In response to one other research:

Knowledge science job postings grew 130% yr over yr after hitting all-time low in July 2023, whereas knowledge analyst openings grew 63% in the identical time interval.

And we are able to additionally see that the wage of information jobs as an entire has been rising over time.

So, it’s clear that knowledge science just isn’t dying by any means; if something, it’s rising.

Nonetheless, why does it really feel very exhausting to get a knowledge scientist job proper now, particularly on the entry and junior ranges?

To elucidate that, we have to look previous the numbers and actually perceive what the fashionable knowledge scientist is.

Knowledge Science Evolution

As an insider on this area, let me inform you a secret.

Knowledge science just isn’t dying; it’s evolving.

10 years in the past, corporations would rent knowledge scientists to tinker with machine studying fashions in Jupyter Notebooks.

In truth, that is precisely what my first knowledge science job was like.

A knowledge scientist was like a Swiss Military Knife — one particular person anticipated to do the whole lot from cleansing knowledge to constructing fashions and presenting to the CEO.

Nonetheless, over time, corporations realised they have been getting no return on funding from this technique, in order that they turned extra stringent about roles and duties to make sure they weren’t losing their cash.

This has led the info science job to grow to be fragmented, and the title has grow to be meaningless, as you’ll find knowledge scientists doing fully completely different jobs at completely different corporations.

On the whole, three flavours of information scientists exist right this moment.

Analyst

One of these knowledge scientist is carefully aligned with the enterprise facet and primarily focuses on reporting workflows and experimentation.

For instance, you’d:

• Get knowledge from an organization database or different sources.
• Write some code that could be very linear and bespoke by nature, beginning with ingesting knowledge, cleansing it a bit, then doing a little EDA and a few inferential or fundamental modelling work.
• As soon as full, you place collectively a report that particulars the evaluation, offers visualisations and different metrics, and affords a suggestion based mostly on the evaluation’s targets.

One of these knowledge scientist is extra of a knowledge analyst and sometimes requires extra enterprise area data.

Engineering

The main target of this sort of knowledge scientist is on constructing and deploying options. This could be a vary of issues like:

• Inner software program tooling
• Machine studying fashions that drive resolution making
• Constructing libraries

This position leans extra towards software program engineering, however in contrast to a software program engineer, it requires higher data of maths, machine studying, and statistics.

These days, this sort of job has moved past the “data scientist” title and is now referred to as a machine studying engineer.

This isn’t entry degree place, and usually requires 2–3 years expertise in an adjoining position like a software program engineer or analyst first. So many graduates and other people with little expertise would wrestle to interrupt into this particular knowledge science place.

Infrastructure

One of these knowledge scientist is the rarest, primarily as a result of it has its personal title: knowledge engineer.

The aim of this position is to construct the info infrastructure and pipelines to deal with the enterprise’s knowledge. This knowledge is then used downstream by machine studying engineers, analysts and even non-technical stakeholders.

This position has grow to be more and more vital, particularly with the emergence of generative AI lately, which requires the power to successfully retailer giant quantities of information and stream it with low latency.

At some corporations, you may additionally be an analytics engineer, which is a extra business-focused knowledge engineer.

I do know, so many titles, its exhausting to maintain up!

Junior vs Senior

A research revealed in September 2025 has been making fairly a number of waves within the knowledge and machine studying house.

The research 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.

Observe: this is applicable not simply to knowledge scientist jobs however to all jobs at these corporations.

You’ll be able to see within the plot beneath that hiring for senior positions continues to be growing, whereas hiring for junior positions is reducing.

This makes intuitive sense, as juniors’ duties are probably simpler to automate with AI than seniors’ because of the wealth of expertise they’ve constructed over time.

What I need to clarify, although, is that corporations aren’t making juniors redundant nor are there no extra junior positions left available on the market. 

Most individuals will take a look at this graph and suppose that the junior knowledge science market is changing into extinct. However that’s objectively not the case.

Hiring continues to be taking place, however the fee of recent positions being posted just isn’t growing. The provision curve stays unchanged whereas demand stays excessive. 

That’s why it feels so exhausting to get an entry-level job these days.

What Can You Do?

I’m going to be sincere, it’s changing into extra aggressive to interrupt into knowledge science, however it’s not inconceivable.

Gone are the times when all you wanted was fundamental Python and SQL, and having achieved Andrew Ng’s Machine Studying course.

These are issues everybody has these days, so it is advisable to go the additional mile and differentiate your self greater than you used to.

There are numerous methods of doing this, for instance, you undertake and concentrate on sure technical domains like:

• GenAI
• Mannequin deployment
• Time collection forecasting
• Advice methods
• Area-specific experience

Specialists are arguably changing into extra vital as data is more and more democratised by AI. Having deep experience is nearly a rarity these days.

Another choice is to go for a lower-level place, like a enterprise or knowledge analyst position, that’s extra pleasant to junior and entry-level positions, after which slowly construct your approach as much as a full-time knowledge scientist place.

You must also give attention to areas that AI can’t actually substitute:

• Speaking successfully with completely different audiences
• Understanding the enterprise affect of your work
• Important pondering and figuring out what drawback to unravel
• Robust fundamentals in maths and statistics
• Relationships and community

These are timeless abilities, particularly the final one.

You might need heard the saying:

It’s not what you already know, however who you know

I really disagree with this.

The true energy is in who is aware of you.

In case you have a strong community and relationship with many individuals within the area who worth and belief you, you’ll be able to faucet into this to get referrals, alternatives, and even develop your community additional.

The leverage this offers is unimaginable. I all the time inform my teaching shoppers that referrals and networks are actually the golden ticket to getting top-end knowledge science jobs.

And all it requires, is simply effort and pushing your self out of your consolation zone to talk to folks you need to join with.

Applied sciences will come and go, however precise human relationships will stay central on your entire profession.

The reality is, you’ll must reinvent your self each 3–5 years as a knowledge scientist, since know-how shifts in a short time.

So asking “Is data science dying?” misses the purpose.

Knowledge science is all the time technically dying because it’s persistently evolving and remodeling.

However that’s what makes it thrilling.

And if you’re keen to up-skill and put in additional effort than others, you’ll be rewarded very effectively.

When you’re able to dive into knowledge science after studying this, that’s an excellent first step. 

However right here’s the truth: I’ve been on this area for 5 years, and searching again, I spent my total first yr on duties that have been a whole waste of time. In right this moment’s hyper-competitive market, you don’t have the luxurious of trial and error.

To keep away from my errors and speed-run your progress, take a look at this information the place I map out precisely how I might grow to be a knowledge scientist once more.