data science was dying 7 months ago?
It was dying again 2 years ago.
And he died 3 years ago.
And not to mention it also died 5 years ago.
However, from where I stand, this is definitely not the case. People still seem to be getting data scientist jobs.
I mean, I help people do this every week in my training program.
So, what's going on in the world?
However, in this article, I want to distinguish:
- What does the current data market look like?
- What does it really mean to be a data scientist
- Also, what should you be doing to get a job in this current climate
Let's get into it!
Market Outlook
As most of you will know, there were massive layoffs between 2022 and 2023, with nearly 90,000 tech workers laid off in January 2023 alone.
In fact, it was so severe that TechCrunch even created an archive of all the layoffs that took place during this time!
However, according to research by 365datascience, data operations were not affected by these layoffs; they found that:
Interestingly, the largest group in our sample of laid-off workers did not have technical jobs – 27.8% worked in HR & Talent Sourcingwhile software engineers entered the second time with 22.1%. Marketing staff followed them with 7.1%, customer service with 4.6%, PR, communications and strategy with 4.4%, etc.
For example, only 2.7% of people hired at Amazon during this period had the title of data scientist.
According to one study:
Data science job postings grew 130% year-over-year after hitting the bottom in July 2023, while data analytics openings grew 63% over the same period.
And we can also see that the salary of data jobs in general is increasing over the years.
So, it is clear that data science is not dying; if anything, it is growing.
However, why does it seem so difficult to find a job in data science right now, especially at the entry and junior levels?
To explain that, we need to look beyond the numbers and really understand what a modern data scientist is.
Data Science Evolution
As an insider in the industry, let me tell you a secret.
Data science is not dying; it appears.
10 years ago, companies were hiring data scientists to run machine learning models on Jupyter Notebooks.
In fact, this is exactly what my first data science job was like.
A data scientist was like a Swiss Army Knife – one person expected to do everything from cleaning data to building models and presenting to the CEO.
However, over time, companies realized that they were not getting a return on investment in this strategy, so they strengthened their roles and responsibilities to ensure that they were not wasting their money.
This has resulted in the data science profession being fragmented, and the title is meaningless, as you will find data scientists doing completely different jobs in different companies.
In general, three flavors of data scientists exist today.
Analyst
This type of data scientist is closely aligned with the business side and focuses mainly on workflow reporting and testing.
For example, you can:
- Obtain data from the company's website or other sources.
- Write some straightforward and descriptive code, start by importing the data, clean it up a bit, and then do some EDA and some basic modeling work.
- When you're done, you compile a report that details the analysis, provides visualizations and other metrics, and provides a recommendation based on the analysis objectives.
This type of data scientist is a data analyst and requires more business domain knowledge.
Engineering
The focus of this type of data scientist is on building and delivering solutions. This could be a range of things like:
- An internal software program
- Machine learning models drive decision making
- Building libraries
This role relies heavily on software engineering, but unlike a software engineer, it requires more knowledge of math, machine learning, and statistics.
These days, this type of work has moved beyond the title of “data science” and is now called a machine learning engineer.
This is not an entry-level position, and typically requires 2–3 years of experience in a related role as a software engineer or analyst first. So many students and people with less experience will struggle to get into this particular field of data science.
Infrastructure
This type of data science is rare, mainly because it has its own title: data engineer.
The goal of this role is to build the data infrastructure and pipelines to store business data. This data is then used downstream by machine learning engineers, analysts or non-technical stakeholders.
This role has become very important, especially with the emergence of productive AI in recent years, which requires the ability to efficiently store large amounts of data and distribute it with low latency.
In some companies, you may also be a statistical engineer, which is a data engineer with a strong business focus.
I know, so many topics, it's hard to keep up!
Junior vs Senior
A study published in September 2025 was making a few waves in the data science and machine space.
The study examined 285,000 companies between 2015 and 2025 and how their adoption of GenAI has affected their hiring processes for junior and senior positions.
Note: this applies not only to data science jobs but to all jobs in these companies.
You can see in the chart below that hiring for senior positions is still growing, while hiring for junior positions is declining.
This makes intuitive sense, as younger people's obligations are probably easier to automate with AI than older people's due to the wealth of experience they have developed over the years.
What I want to make clear, however, is that companies are not making juniors redundant and there are no junior positions left in the market.
Most people will look at this graph and think that the small data science market is disappearing. But that is not the case.
Hiring continues, but the rate of new job postings is not increasing. The supply curve remains constant while demand remains high.
That's why it feels so hard to find an entry-level job these days.
What Can You Do?
I'll be honest, it's getting very competitive to get into data science, but it's not impossible.
Gone are the days when all you needed was basic Python and SQL, and you've finished Andrew Ng's Machine Learning course.
These are things that everyone has these days, so you need to go the extra mile and differentiate yourself more than ever.
There are many ways to do this, for example, using and focusing on specific technical domains such as:
- GenAI
- Model submission
- Time series forecasting
- Recommendation programs
- Domain-specific technology
Experts are arguably more important as information is increasingly democratized by AI. Having deep expertise is almost a rarity these days.
Another option is to go for a lower-level position, such as a business or data analyst role, which is more friendly to entry-level and entry-level positions, and gradually build up to a full-time data scientist position.
You should also focus on the areas that AI can really change:
- Effective communication with different audiences
- Understanding the business impact of your work
- Critical thinking and knowing what problem to solve
- A strong foundation in mathematics and statistics
- Relationships and networking
These are infinite abilities, especially the last one.
You may have heard the saying:
It's not what you know, but who you know
I actually disagree with this.
The real power is within who knows.
If you have a strong network and relationships with many people in the role who value and trust you, you can tap into this for referrals, opportunities, or expand your network further.
The benefit this provides is amazing. I always tell my coaching clients that referrals and networking are actually a great ticket to landing top data science jobs.
And all you need is effort and pushing yourself out of your comfort zone to talk to the people you want to connect with.
Technology will come and go, but real human relationships will always be important throughout your career.
The truth is, you will need to reinvent yourself every 3-5 years as a data scientist, as technology changes so fast.
So he asked “Is data science dying?” you miss the point.
Data science is always evolving technologically as it constantly changes and evolves.
But that's what makes it fun.
And if you're willing to step up and put in more effort than others, you'll be rewarded handsomely.
If you're ready to get into data science after reading this, that's a great first step.
But here's the truth: I've been in this industry for five years, and looking back, I spent my entire first year doing jobs that were a total waste of time. In today's highly competitive market, you don't have the luxury of trial and error.
To avoid my mistakes and speed up your progress, check out this guide where I break down exactly how to become a data scientist again.
One thing!
Join my free newsletter where I share weekly tips, insights, and advice from my experience as a practicing data scientist and machine learning engineer. And, as a subscriber, you will receive mineFREE Resume Template!
Data Extraction
Weekly emails help you find your first job in data science or machine learningnewsletter.egorhowell.com
