Is the AI and Knowledge Job Market Lifeless?

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 positively not the case. Individuals nonetheless appear to land information scientist jobs.

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

So, what on earth is happening?

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

• What the present information market appears like
• What it truly 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 a lot of you’ll know, there have been vital layoffs throughout 2022 and 2023, with practically 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!

Nevertheless, in line with 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.

Based on one other research:

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 will additionally see that the wage of information jobs as a complete has been rising over time.

So, it’s clear that information science just isn’t dying in anyway; if something, it’s rising.

Nevertheless, why does it really feel very arduous 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 area, let me let you know a secret.

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

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

In truth, 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.

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 obligations to make sure they weren’t losing their cash.

This has led the information science job to grow to be fragmented, and the title has grow to be meaningless, as you will see information scientists doing fully completely different jobs at completely different firms.

Basically, three flavours of information scientists exist right now.

Analyst

Such a information scientist is carefully aligned with the enterprise aspect 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 performing some EDA and a few inferential or fundamental modelling work.
• As soon as full, you place collectively a report that particulars the evaluation, gives visualisations and different metrics, and presents a suggestion primarily based on the evaluation’s targets.

Such a information scientist is extra of a knowledge analyst and usually requires extra enterprise area information.

Engineering

The main target of one of these 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 larger information of maths, machine studying, and statistics.

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

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

Infrastructure

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

The objective of this function 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 function has grow to be more and more essential, particularly with the emergence of generative AI in recent times, which requires the flexibility to successfully retailer massive quantities of information 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 research printed in September 2025 has been making fairly just a few 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’ll be able to see within the plot beneath that hiring for senior positions remains to be rising, whereas hiring for junior positions is reducing.

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

What I wish 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 take 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 price of latest positions being posted just isn’t rising. 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 unattainable.

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 you must 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 concentrate on 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 nearly 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 approach as much as a full-time information scientist place.

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

• Speaking successfully with completely different audiences
• Understanding the enterprise affect of your work
• Vital pondering and understanding what downside to resolve
• Robust fundamentals in maths and statistics
• Relationships and community

These are timeless expertise, particularly the final one.

You may need heard the saying:

It’s not what , however who you know

I truly disagree with this.

The true energy is in who is aware of you.

You probably 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 gives is unimaginable. I at all times 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 folks you wish to join with.

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

The reality is, you will must reinvent your self each 3–5 years as a knowledge scientist, since expertise shifts in a short time.

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

Knowledge science is at all times technically dying because it’s constantly evolving and reworking.

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.

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

However right here’s the fact: I’ve been on this area for 5 years, and searching again, I spent my whole first yr on duties that have been a whole waste of time. In right now’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 grow to be a knowledge scientist once more.