Is the AI and Data Job Market Dead?
Source: Towards Data Science
# Is Data Science Really Dying?
It’s a claim you’ve probably heard a few times:
- **7 months ago**
- **2 years ago**
- **3 years ago**
- **5 years ago**
---
## The Reality
From where I stand, the data‑science job market is **far from dead**. Companies continue to hire data scientists, and I help people break into the field every week through my coaching program:
- **[Coaching Program – Egor Howell](https://coaching.egorhowell.com/)**
So, what’s really happening?
---
## In This Article
I’ll break down three key topics:
1. **What the current data market looks like**
2. **What it actually means to be a data scientist**
3. **What you should be doing to land a job in today’s climate**
Let’s dive in! Market Outlook
As many of you know, the tech sector experienced massive layoffs in 2022‑2023, with nearly 90 000 tech employees laid off in January 2023 alone【1】.
TechCrunch even created an archive of all layoffs that occurred during this period【2】.
Are data jobs being hit?
A study by 365 Data Science found that data‑related roles were relatively insulated:
“Interestingly, our sample’s largest group of laid‑off employees did not hold tech jobs — 27.8 % worked in HR & Talent Sourcing, while software engineers came in second with 22.1 %. Marketing employees followed with 7.1 %, customer service with 4.6 %, PR/communications/strategy with 4.4 %, etc.”【3】
- Only 2.7 % of Amazon’s layoffs were data scientists.
Demand for data talent is rising
Another recent report shows a sharp rebound in hiring:
- Data‑science job postings grew 130 % YoY after hitting a low in July 2023.
- Data‑analyst openings grew 63 % YoY over the same period【4】.
Source: Interview Query – August 2024 Data‑Science Job Market Report
Salaries for data roles have also been on an upward trend:
Source: Interview Query – Data‑Scientist Salary Data
Bottom line: Data science is not dying; the field is actually growing.
Why does landing a data‑scientist role feel so hard right now?
To answer that, we need to look beyond the raw numbers and examine what the modern data scientist actually does.
References
- TechCrunch – “A comprehensive archive of 2023 tech layoffs” (Jan 2023 layoffs). https://techcrunch.com/2024/05/01/a-comprehensive-archive-of-2023-tech-layoffs/#janlayoffs
- TechCrunch – Archive of 2023 tech layoffs. https://techcrunch.com/2024/05/01/a-comprehensive-archive-of-2023-tech-layoffs/
- 365 Data Science – Who was affected by the 2022‑2023 tech layoffs? https://365datascience.com/trending/who-was-affected-by-the-2022-2023-tech-layoffs/
- Interview Query – August 2024 Data‑Science Job Market Report. https://www.interviewquery.com/p/august-data-science-job-market-report-2024
Data Science Evolution
As an insider in this field, let me share a secret: data science isn’t dying—it’s evolving.
Ten years ago, companies hired data scientists to tinker with machine learning models in Jupyter Notebooks. My first data‑science role was exactly that. A data scientist was a Swiss‑army‑knife—one person expected to clean data, build models, and present findings to the CEO.
Over time, organizations realized that this “jack‑of‑all‑trades” approach delivered little ROI. Roles became more specialized, and the title “data scientist” grew increasingly ambiguous. Today, three main “flavours” of data scientists exist.
1. Analyst
Business‑oriented; focuses on reporting and experimentation.
Typical responsibilities:
- Retrieve data from company databases or external sources.
- Write linear, bespoke code to ingest, clean, explore (EDA), and perform basic inferential or predictive modeling.
- Produce a report that includes visualisations, key metrics, and actionable recommendations.
This role leans toward a data analyst and usually requires strong domain knowledge.
2. Engineering
Builds and deploys data‑driven solutions.
Common tasks:
- Develop internal software tools.
- Deploy machine‑learning models that drive decision‑making.
- Create reusable libraries.
The engineering track is more software‑engineer‑like, but it demands deeper expertise in mathematics, machine learning, and statistics. In many organisations this role is now called Machine Learning Engineer.
It’s not an entry‑level position; 2–3 years of experience as a software engineer or analyst is typically required. Graduates and those with limited experience often find it hard to break into this path.
3. Infrastructure
The rarest “data‑scientist” role, usually titled Data Engineer.
Key responsibilities:
- Design and maintain data infrastructure and pipelines.
- Ensure data is reliably stored, transformed, and made available to downstream users (ML engineers, analysts, or non‑technical stakeholders).
- Support high‑throughput, low‑latency data flows—especially critical with the rise of generative AI, which demands massive, fast‑moving datasets.
Some companies also have an Analytics Engineer, a hybrid role that blends data‑engineering skills with a business focus.
I know—so many titles! It’s hard to keep up.
Junior vs Senior
A study published in September 2025 has been making quite a few waves in the data‑and‑machine‑learning space.
The study examined 285,000 companies between 2015 and 2025 and looked at how their adoption of GenAI has affected hiring for junior and senior positions.
Note: This applies not just to data‑scientist jobs but to all jobs at these companies.
Findings
- Hiring for senior positions is still increasing.
- Hiring for junior positions is decreasing.
You can see this trend in the plot below:
Source: SSRN paper 5425555.
Interpretation
It makes intuitive sense: junior responsibilities are often easier to automate with AI than senior ones, given the wealth of experience seniors have built over the years.
However, it’s important to clarify that companies aren’t making junior roles redundant, nor have junior positions disappeared.
- Hiring is still happening for junior roles.
- The rate of new positions being posted is not increasing.
- The supply curve remains unchanged while demand stays high.
That’s why it feels so hard to get an entry‑level job nowadays.
What Can You Do?
I’m going to be honest: breaking into data science is getting more competitive, but it’s not impossible.
Gone are the days when basic Python, SQL, and Andrew Ng’s Machine Learning course were enough. Those skills are now commonplace, so you need to go the extra mile and differentiate yourself.
1. Specialise in Technical Domains
Pick a niche and become an expert. Some high‑impact areas include:
- Generative AI (GenAI)
- Model deployment
- Time‑series forecasting
- Recommendation systems
- Domain‑specific expertise (e.g., healthcare, finance, e‑commerce)
Specialists are increasingly valuable as AI democratises general knowledge. Deep expertise is becoming a rarity.
2. Take a Lower‑Level, Business‑Focused Role
A business analyst or data analyst position is often more junior‑friendly. It lets you:
- Gain real‑world experience with data pipelines and stakeholder communication
- Build a portfolio of impact‑driven projects
- Transition later into a full‑time data‑science role
3. Focus on Skills AI Can’t Replace
| Human‑Centred Skill | Why It Matters |
|---|---|
| Effective communication with diverse audiences | Turns insights into action |
| Understanding business impact of your work | Aligns projects with company goals |
| Critical thinking – knowing which problem to solve | Prevents wasted effort |
| Strong fundamentals in maths & statistics | Guarantees sound modelling |
| Relationships & networking | Opens doors to opportunities |
These timeless abilities—especially networking—remain crucial.
“It’s not what you know, but who you know.”
I disagree. The real power lies in who knows you.
If you have a solid network of trusted professionals, you can:
- Get referrals
- Discover hidden opportunities
- Expand your own network further
I tell my coaching clients that referrals and relationships are literally the golden ticket to top‑end data‑science jobs. All it takes is effort and the willingness to step out of your comfort zone to connect with the right people.
Technology will come and go; human relationships endure.
4. Embrace Continuous Reinvention
The data‑science landscape shifts every 3–5 years. Asking “Is data science dying?” misses the point—the field is constantly evolving, which makes it exciting.
If you’re willing to up‑skill and put in more effort than others, you’ll be rewarded handsomely.
Next Steps
If you’re ready to dive into data science, that’s a great first step. But remember:
- My first year in the field was spent on tasks that added little value.
- In today’s hyper‑competitive market, you don’t have the luxury of trial‑and‑error.
Avoid my mistakes and speed‑run your progress by checking out the guide where I map out exactly how I would become a data scientist again.
[Download the “Fast‑Track Data Scientist” guide] (link placeholder)
Another Thing!
Join my free newsletter where I share weekly tips, insights, and advice from my experience as a practising data scientist and machine learning engineer. Plus, as a subscriber you’ll get my FREE Resume Template!
Dishing The Data – Weekly emails helping you land your first job in data science or machine learning