Data scientists won't exist in 5 years
The quiet unbundling of the sexiest job of the 21st century
The job title “Data Scientist” will be extinct in five years. This isn’t another doomsday prediction about the rise of AI. This is about a market correction that’s already happening in the consolidation of jobs that don’t have a clear focus. We spoke about the role of a data engineer being overloaded as two jobs but now let’s discuss a role that was so ambiguous it was setup to fail - one that makes us wonder, was a data scientist ever a role at all?
The “sexiest job of the 21st century” is a lie
Back in 2012, an article came out about the role of a ‘data scientist’ - someone who “has the training and curiosity to make discoveries in the world of big data”. It was noted that this skillset was rare and decreed the role as the “sexiest job of the 21st century”. As people flocked to this career path, the role was declared a “unicorn” and a dreaded Venn diagram was created to capture the overlap of the three key skills:
software engineering
statistics
business acumen
The role took off because it was something different and fun. Who doesn’t love to predict the future? Everyone wanted to believe that data scientists were the mythical hero who could do it all. However, data in the corporate world was their kryptonite - while in theory it was clean like iris flower data - the reality was more like overgrown weeds and fallen trees. This proved to be a stumbling block to getting started but the enthusiasm to hire data scientists didn’t slow. Yet over a decade later, why do 80% of data science projects fail?
The problem isn’t the data scientist, it’s the role - aka it’s not you, it’s me
The answer isn’t the practitioner - it’s the job description. The role itself is a paradox. What started out as the universal skill for a data scientist was “the ability to write code” but it quickly became a catch-all term for three distinct functions.
1. The data cleaner
The bulk of the work is wrangling data into a usable format.
This is the job of an Analytics Engineer.
2. The pipeline builder
The task of getting data to a model and getting predictions out.
This is the job of a Data Platform Engineer or ML Engineer.
3. The storyteller
Being able to explain the insights and link it to the business problem or need.
This is the job of a Data Analyst.
Notice what’s missing? “science”...
The science that was really just vibes
At this point, you might be thinking “But what about the ML models? Didn’t the data scientists create those?”. And you’d be right. However, while that was the glamorous side of the job, it generally accounted for just 10-20% of a data scientist’s time.
When you unpack the ‘science’ further, it starts to unravel more:
Algorithm Choice: This isn’t a mystical quest. It’s a dependency choice, just like a software engineer choosing between React and Vue (or the other thousand Javascript packages). Why do people spend three weeks testing Random Forest vs XGBoost for a 2% accuracy bump that translates to zero business impact?
Data Drift: This is portrayed as a novel scientific challenge but it’s just a data quality and monitoring problem. Analytics Engineers have been handling schema changes and slowly changing dimensions for years.
Build vs Buy: The decision to build a custom model is not a scientific one. It’s a classic engineering build vs buy analysis. For most use cases, it doesn’t make sense to build your own, since off-the-shelf models or AutoML solutions will usually suffice. However, if a 0.01 uplift in AUC translates to a $1M return, then the investment and ongoing maintenance might be justified.
All that’s left is a process driven more by intuition than evidence. What we called data science was often just trial and error in a Jupyter notebook - vibe coding at a PhD price.
Will the real model owner please stand up?
Unlike traditional engineering which was delivering in small increments, data science often involved large upfront time investment called discovery or experimentation. This created friction between executives keen for results and the data scientist grinding through experiments.
Things went sideways as the data scientist become too technical-focused on the ‘science’ rather than the actual business problem. Disconnected from the business outcome, it became a negative feedback loop and more time was spent tuning the the model accuracy rather than validating the outcomes.
Thus, the ownership of the model became unclear as the business person asking for the help didn’t understand how the 5 layer neural network predicted customer A would churn. Neither did the data scientist understand the impact of a customer leaving the business. This left the model running in production but unused. And this was a good case scenario.
Often the model hardly ever made production due to hand-offs. At one place I worked, the data scientist wrote a model in python. However, the ML framework was written in Java. So an engineer had to rewrite the model in Java that the data scientist had created in Python. This is crazy! Imagine McDonalds creating two hamburgers for the one you ordered. It’s no wonder the executives were wondering why ML initiatives took so long with situations like these.
The Data Science Unbundling: what comes next
Data scientists won’t exist in five years - not because of AI - but because the work splits cleanly into two real jobs: product analytics and engineering. The rest is a vibes-based middle where notebooks flourish, models drift and nobody owns outcomes.
The roles that actually matter:
Data Analysts: Own the understanding and explanation of data
Analytics Engineers: Own the semantic layer and deliver trusted, clean data
ML Engineers: Own the end-to-end process of deploying, scaling, and monitoring models as a product
(Optional) Research Scientists: The true scientists, reserved for the 1% of novel problems where off-the-shelf solutions don’t exist
We’re not saying that the people who were Data Scientists aren’t valuable. Quite the opposite. This unbundling is actually a promotion - where there is increased focus, accountability and, most importantly, impact.
If you’re a Data Scientist: Audit your last three months. Where did you create tangible value? Double down on that - whether it’s engineering or analytics - and rebrand yourself.
If you’re a leader: Stop writing unicorn job descriptions. Redefine your roles for clarity and ownership. Your team’s velocity and the business’s profit margins will thank you.
The “Data Scientist” title is a failed experiment born from a decade of hype. While the job title joins Data Entry Clerks gathering dust, the future belongs to those who make data impactful.