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.

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

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

So, what on earth is occurring?

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

• What the present information market seems to be like
• What it really means to be a knowledge scientist
• And, what you have 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 important layoffs throughout 2022 and 2023, with almost 90,000 tech workers being laid off in January 2023 alone.

In reality, 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, information jobs weren’t that affected by these layoffs; they discovered that:

Curiously, 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 and marketing workers adopted them with 7.1%, customer support with 4.6%, PR, communications & technique with 4.4%, and many others.

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

In keeping with one other study:

Knowledge science job postings grew 130% 12 months over 12 months 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 information jobs as an entire has been rising through the years.

So, it’s clear that information science is just not dying in anyway; 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 trendy 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.

In reality, that is precisely what my first information science job was like.

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

Nonetheless, over time, firms realised they had been getting no return on funding from this technique, so that they turned extra stringent about roles and tasks to make sure they weren’t losing their cash.

This has led the information science job to develop into fragmented, and the title has develop into meaningless, as you can find information scientists doing fully totally different jobs at totally different firms.

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

Analyst

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

For instance, you’d:

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

The sort of information scientist is extra of a knowledge analyst and sometimes requires extra enterprise area information.

Engineering

The main target of such a information scientist is on constructing and deploying options. This is usually a vary of issues like:

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

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

These days, such a job has moved past the “information scientist” title and is now referred to as a machine studying engineer.

This isn’t entry stage place, and usually requires 2–3 years expertise in an adjoining position 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 objective of this position is to construct the information infrastructure and pipelines to deal with the enterprise’s information. This information is then used downstream by machine studying engineers, analysts and even non-technical stakeholders.

This position has develop into more and more essential, particularly with the emergence of generative AI lately, which requires the power to successfully retailer massive quantities of information and stream it with low latency.

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

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

Junior vs Senior

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

The research 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.

Be aware: 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 reducing.

This makes intuitive sense, as juniors’ tasks are possible simpler to automate with AI than seniors’ because of the wealth of expertise they’ve constructed through the years.

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 taking place, however the charge of recent positions being posted is just not 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 trustworthy, it’s changing into extra aggressive to interrupt into information science, however it’s not inconceivable.

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

These are issues everybody has these days, so you’ll want to go the additional mile and differentiate your self greater than you used to.

There are a lot of methods of doing this, for instance, you undertake and specialize in sure technical domains like:

• GenAI
• Mannequin deployment
• Time collection forecasting
• Suggestion techniques
• 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 position, that’s extra pleasant to junior and entry-level positions, after which slowly construct your manner as much as a full-time information scientist place.

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

• Speaking successfully with totally different audiences
• Understanding the enterprise influence of your work
• Important considering and figuring out what downside 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 , however who you know

I really disagree with this.

The actual energy is in who is aware of you.

When you have a strong 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 broaden your community additional.

The leverage this gives is unbelievable. I all the time inform my teaching purchasers 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 individuals 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 have to reinvent your self each 3–5 years as a knowledge 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 constantly evolving and reworking.

However that’s what makes it thrilling.

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

In case you’re able to dive into information science after studying this, that’s an amazing first step. 

However right here’s the fact: I’ve been on this subject for 5 years, and searching again, I spent my whole first 12 months on duties that had been a whole waste of time. In at the 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’d develop into a knowledge scientist once more.