Data Science in 2026: Is It Still Worth It?

about switching to Data Science in 2026?

If the answer is “yes,” this article is for you.

I’m Sabrine. I have spent the last 10 years working in the AI field across Europe—from big companies and startups to research labs. And if I had to start over again today, I would honestly still choose this field. Why?

For the same reasons that brought many of us here: the intellectual challenge, the impact you can have, the love for mathematics and code, and the possibility to solve real-life problems.

But looking toward 2026… is it still worth it?

If you scroll through LinkedIn, you will see two teams fighting: one saying “Data Science is dead,” and the other saying it is growing thanks to the AI trend.

When I look around me, I personally think we will always need computational skills. We will always need people who can understand data and help make decisions. Numbers have always been everywhere, and why would they disappear in 2026?

However, the market has changed. And to navigate it now, you need good guidance and clear information.
In this article, I’ll share my own experience from working in research and industry, and from mentoring more than 200 Data Scientists over the last few years.

---

So what is happening in the market now?

I will be honest and not sell you any dream about it.
The goal is not to introduce biases, but to give you enough information to make your own decision.

Is the Data Science job family broader than ever?

One of the biggest mistakes of junior Data Scientists is thinking Data Science is one single job.

In 2026, Data Science is a large family of roles. Before writing a single line of code, you need to understand where you fit.

People are fascinated by AI: how ChatGPT talks, how Neuralink stimulates brains, and how algorithms affect health and security. But let’s be honest: not all aspiring Data Scientists will build these types of projects.

These roles need strong applied math and advanced coding skills. Does that mean you will never reach them? No. But they are often for people with PhDs, computational scientists, and engineers trained exactly for these niche jobs.

Let’s take a real example: a Machine Learning/Data Scientist job offer I saw today (Nov 27) at a GAFAM company.

If you look at the description, they ask for:

• Patents
• First-author publications
• Research contributions

Does everyone interested in Data Science have a patent or a publication? Of course not.

This is why you must avoid moving blindly.

If you just finished a bootcamp or are early in your studies, applying for jobs that explicitly require research publications will only bring frustration. These very specialized jobs are usually for people with advanced academic backgrounds (PhD, post-doc, or computational engineering).

My advice: be strategic. Focus on roles that match your skills.
Don’t waste time applying everywhere.

Use your energy to build a portfolio that aligns with your goals.

You must understand the different sub-fields inside Data Science and choose what fits your background. For example:

• Product Data Analyst / Scientist: product lifecycle and user needs
• Machine Learning Engineer: deploying models
• GenAI Engineer: works on LLMs
• Classic Data Scientist: inference and prediction

If you look at a Product Data Scientist role at Meta, the technical level is often more adapted to most Data Scientists on the market compared to a Core AI Research Engineer or Senior Data Scientist role.

These roles are more realistic for someone without a PhD.

Even if you don’t want to work at GAFAM, keep in mind:

They set the direction. What they require today becomes the norm everywhere else tomorrow.

---

Now, how about coding and math in 2026?

Here is a controversial but honest truth for 2026: Analytical and mathematical skills matter more than just coding.

Why? Almost every company now uses AI tools to help write code. But AI cannot replace your ability to:

• understand trends
• explain where the value comes from
• design a valid experiment
• interpret a model in a real context

Coding is still important, but you cannot be a “General Importer”—someone who only imports sklearn and runs .fit() and .predict().

Very soon, an AI agent may do that part for us.
But your math and analytical skills are still important, and will always be.

A simple example:
You can ask an AI: “Explain PCA like I’m 2 years old.”

But your real value as a Data Scientist comes when you ask something like:

“I need to optimize the water production of my company in a specific region. This region is facing issues that make the network unavailable in specific patterns. I have hundreds of features about this state of the network. How can I use PCA and be sure the most important variables are represented in the PC I’m using?”

-> This human context is your value.
-> AI writes the code.
-> You bring the logic.

---

And how about the Data Science toolbox?

Let’s start with Python. As a programming language with a large data community, Python is still essential and probably the first language to learn as a future Data Scientist.

The same for Scikit-learn, a classic library for machine learning tasks.

We can also see on Google Trends (late 2025) that:

• PyTorch is now more popular than TensorFlow
• GenAI integration is growing much faster than classical libraries
• Data Analyst interest stays stable
• Data Engineer and AI Specialist roles interested more people than general Data Scientist roles

Don’t ignore these patterns; they are very helpful for making decisions.

You need to stay flexible.

If the market wants PyTorch and GenAI, don’t stay stuck with only Keras and old NLP.

---

And what about the new stack for 2026?

This is where the 2026 roadmap is different from 2020.
To get hired today, you need to be production-ready.

Version Control (Git): You will use it daily. And to be honest, this is one of the first skills you need to learn at the beginning. It helps you organize your projects and everything you learn.

Whether you are starting a Master’s program or beginning a bootcamp, please don’t forget to create your first GitHub repository and learn a few basic commands before going further.

AutoML: Understand how it works and when to use it. Some companies use AutoML tools, especially for Data Scientists who are more product-oriented.

The tool I have in mind, and that you can access for free, is Dataiku. They have a great academy with free certifications. It is one of the AutoML tools that has exploded in the market in the last two years.
If you don’t know what AutoML is: it is a tool that lets you build ML models without coding. Yes, it exists.

Remember what I said earlier about coding? This is one of the reasons why other skills are becoming more important, especially if you are a product-oriented Data Scientist.

MLOps: Notebooks are not enough anymore. This applies to everyone. Notebooks are good for exploration, but if at some point you need to deploy your model in production, you must learn other tools.

And even if you don’t like data engineering, you still need to understand these tools so you can communicate with data engineers and work together.

When I talk about this, I think about tools like Docker (check out my article), MLflow (link here), and FastAPI.

LLMs and RAG: You don’t need to be an expert, but you should know the basics: how the LangChain API works, how to train a small language model, what RAG means, and how to implement it. This will really help you stand out in the market and maybe move further if you need to build a project that involves an AI Agent.

---

Portfolio: Quality over quantity

In this fast and competitive market, how can you prove you can do the job? I remember I’ve written an article about how to create a portfolio 2 years ago and what I’m going to say here can look a bit contradictory, but let me explain. Before ChatGPT and AI tools flooded the market, having a portfolio with a bunch of projects to show your different skills like data cleaning and data processing was very important, but today all these basic steps are often done using AI tools that are ready for that, so we will focus more on building something that will make you different and make the recruiter want to meet you.

I would say: “Avoid burnout. Build smart.”

Don’t think you need 10 projects. If you’re a student or a junior, one or two good projects are enough.

Take advantage of the time you have during your internship or your final bootcamp project to build it. Please don’t use simple Kaggle datasets. Look online: you can find a huge amount of real use-case data, or research datasets that are more often used in industry and labs to build new architectures.

If your goal is not to go deep into the technical side, you can still show other skills in your portfolio: slides, articles, explanations of how you thought about the business value, what results you got, and how these results can be used in reality. Your portfolio depends on the job you want.

• If your goal is more math-oriented, the recruiter will probably want to see your literature review and how you implemented the latest architecture on your data.
• If you are more product-oriented, I would be more interested in your slides and how you interpret your ML results than in the quality of your code.
• If you are more MLOps-oriented, the recruiter will look at how you deployed, monitored, and tracked your model in production.

To finish, I want to remind you that the market is changing fast, but it is not the end of Data Science. It just means you need to be more aware of where you fit, what skills you want to grow, and how you present yourself.

Keep learning, and build a portfolio that truly reflects who you are. You will find your place ❤️

If you enjoyed this article, feel free to follow me on LinkedIn for more honest insights about AI, Data Science, and careers.

👉 LinkedIn: Sabrine Bendimerad
👉 Medium: https://medium.com/@sabrine.bendimerad1