Hey,

With tools like ChatGPT, Claude, and Deepseek getting smarter and faster, it’s easy to wonder: do we humans still matter in this whole AI thing?

Honestly, I had the same thought. I read an interview where NVIDIA’s CEO said “coding is dead,” and even Zuckerberg claimed that by 2025, AI agents might be as good as mid-level software engineers.

Sounds scary, right? But here’s the truth:

Data science isn’t dying — it’s evolving.

We’re not being replaced. We’re being challenged to grow.

It’s pretty much the only industry that has a future, don’t listen to the lies on the internet, the layoffs are mostly due to a natural downturn in the market, it’s been happening forever and it happens in every industry.

And if you’re a data scientist (or becoming one), this shift might be the biggest opportunity you’ll see in your career.

The “traditional” data science role barely ever existed. The number of businesses with the type of problems and data where you need data science are very few. But data was the new hotness and FAANG (Facebook, Apple, Amazon…) uses it, so businesses started hiring data scientists. So ‘data science’ as a business term often was shorthand for something like ‘guru rockstar ninja business intelligence person’. Or maybe ‘full stack data person’ in some cases.

And now we’re on the wrong side of the hype cycle and businesses are being more careful about how they throw money into data, and hirers can be more picky and don’t need to use a flash term like scientist to entice applicants. So the fake data science roles aren’t as common.

I doubt there’s a drop-off of actual data science getting done in those companies that have a genuine use for it. But there are definitely fewer roles advertised for data science, which is why I think it’s mostly the ‘fake’ data science roles that have gone.

The stuff that got you hired 5 years ago won’t cut it anymore. But if you’re willing to learn and adapt, you’ll do just fine. Here’s how to stand out:

1. Always Be Learning

If you stop learning, you fall behind. That simple.

How to stay sharp:

• Follow smart people on LinkedIn, Medium, Substack.
• Take courses in AI, cloud, and MLOps.
• Play with tools like LangChain, AutoML, or vector databases.

2. Learn Business, Not Just Code

It’s not about fancy models. It’s about solving real problems. Do this:

• Figure out what KPIs matter in your industry.
• Talk to marketing/sales teams to understand pain points.
• Build stuff that helps them make decisions, not just dashboards.

3. Build Your Name

You don’t need 100k followers. But if no one knows what you’re doing, you’re invisible. Try this:

• You don’t need 100k followers. But if no one knows whShare tips or lessons online (even messy ones).
• Contribute to GitHub projects.
• Speak at local meetups or webinars — it builds trust.at you’re doing, you’re invisible. Try this:

4. Use AI Like a Teammate

Don’t fight the wave. Try:

• Using ChatGPT to clean up or write code faster.
• Exploring AutoML tools to save time.
• Doubling down on things AI can’t do: judgment, context, creativity.

5. Keep It Practical

Theory is cool. But projects get you hired. So:

• Build stuff that solves real problems.
• Go from start to finish — not just model accuracy, but delivery.
• Show it off. A strong portfolio = job magnet.

Every time a shiny new tech shows up, people panic. It’s been the same story since the Internet showed up — “Is this thing going to take my job?” And now with AI, it’s déjà vu all over again. But here’s the thing: as long as companies are drowning in data (which they are, more than ever), they’re still going to need people who know how to make sense of it.

Will the role change? Yeah, of course. It should. Tech evolves, and so should the people using it. It’s tempting to fear AI slop, because it’s here and it’s going to get worse. But there’s human slop all over the internet, and it’s getting worse as well.

Whatever you do, the goal is the same: create real value for the people who need it. Do work that matters for people who care.