Is the AI and Data Job Market Dead?

information 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.

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

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

So, what on earth is happening?

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

• What the present information market appears like
• What it truly means to be an information scientist
• And, what you ought to be doing to land a job on this present local weather

Let’s get into it!

Market Outlook

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

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

Nevertheless, in keeping with a examine by 365datascience, information jobs weren’t that affected by these layoffs; they discovered that:

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

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

Based on one other study:

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

And we are able to additionally see that the wage of knowledge jobs as a complete has been rising over time.

So, it’s clear that information science is just not dying by any means; if something, it’s rising.

Nevertheless, why does it really feel very arduous to get an information 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 information scientist is.

Knowledge Science Evolution

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

Knowledge science is just not dying; it’s evolving.

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

The truth is, that is precisely what my first information science job was like.

A knowledge scientist was like a Swiss Military Knife — one individual anticipated to do all the pieces from cleansing information to constructing fashions and presenting to the CEO.

Nevertheless, over time, firms 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 information science job to turn out to be fragmented, and the title has turn out to be meaningless, as you’ll discover information scientists doing fully totally different jobs at totally different firms.

On the whole, three flavours of knowledge scientists exist as we speak.

Analyst

The sort of information scientist is carefully aligned with the enterprise facet and primarily focuses on reporting workflows and experimentation.

For instance, you’ll:

• Get information from an organization database or different sources.
• Write some code that may be very linear and bespoke by nature, beginning with ingesting information, cleansing it a bit, then performing some EDA and a few inferential or primary modelling work.
• As soon as full, you set collectively a report that particulars the evaluation, supplies visualisations and different metrics, and affords a advice based mostly on the evaluation’s targets.

The sort of information scientist is extra of an information analyst and usually requires extra enterprise area information.

Engineering

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

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

This function leans extra towards software program engineering, however in contrast to a software program engineer, it requires better information of maths, machine studying, and statistics.

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

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

Infrastructure

The sort of information scientist is the rarest, primarily as a result of it has its personal title: information engineer.

The purpose of this function is to construct the information infrastructure and pipelines to accommodate the enterprise’s information. This information is then used downstream by machine studying engineers, analysts and even non-technical stakeholders.

This function has turn out to be more and more essential, particularly with the emergence of generative AI in recent times, which requires the flexibility to successfully retailer giant quantities of knowledge and stream it with low latency.

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

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

Junior vs Senior

A study published in September 2025 has been making fairly just a few waves within the information and machine studying house.

The examine examined 285,000 firms 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 information scientist jobs however to all jobs at these firms.

You may see within the plot under that hiring for senior positions remains to be growing, whereas hiring for junior positions is lowering.

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

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

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

Hiring remains to be occurring, however the price of recent positions being posted is just not growing. The provision curve stays unchanged whereas demand stays excessive. 

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

What Can You Do?

I’m going to be trustworthy, it’s changing into extra aggressive to interrupt into information science, however it’s not not possible.

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

These are issues everybody has these days, so it’s good 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 programs
• Area-specific experience

Specialists are arguably changing into extra essential as information is more and more democratised by AI. Having deep experience is sort of a rarity these days.

An alternative choice is to go for a lower-level place, like a enterprise or information analyst function, that’s extra pleasant to junior and entry-level positions, after which slowly construct your means as much as a full-time information scientist place.

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

• Speaking successfully with totally different audiences
• Understanding the enterprise influence of your work
• Vital considering and realizing what drawback to resolve
• Sturdy fundamentals in maths and statistics
• Relationships and community

These are timeless abilities, particularly the final one.

You may need heard the saying:

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

I truly disagree with this.

The actual energy is in who is aware of you.

In case you have a stable community and relationship with many individuals within the subject who worth and belief you, you may faucet into this to get referrals, alternatives, and even increase your community additional.

The leverage this supplies is unimaginable. I all the time inform my teaching shoppers that referrals and networks are actually the golden ticket to getting top-end information 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 in your entire profession.

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

So asking “Is information 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 prepared to up-skill and put in additional effort than others, you can be rewarded very effectively.

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

However right here’s the fact: I’ve been on this subject for 5 years, and looking out again, I spent my whole first yr on duties that have been a whole waste of time. In as we speak’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’d turn out to be an information scientist once more.