How AI is splitting data jobs: Who wins — and who disappears?

An experienced director of a 600-person data organization speaks openly about the new reality: Most junior data analysts have barely any chances anymore. Mid-level professionals are under pressure. But those who think correctly are experiencing the best year of their careers. What you can do specifically.

The uncomfortable question: Are you a "human-machine interface"?

There's a question that's been shifting the mood in many data teams over the last few months. It's as simple as it is harsh: If the business orders a metric — you take the request, write an SQL statement, return the number. What intellectual contribution have you made? The answer is: none. You were a translator between a business question and a programming language. An experienced data director puts it bluntly:

If that's all you do, then I'm sorry. You should seriously consider your future.

This is not cynical. This is the sober observation of someone who is currently running an experiment within their organization: AI agents versus live analysts. And the result will surprise you.

The test that changed everything

A specific business task: Warehouse pricing. The peculiarity of this task has a catch – when a warehouse is 50 percent occupied, every additional sale is practically pure profit. The warehouse is there anyway. However, if the warehouse is 100 percent full, prices must be increased. In other words, the most important thing you need to figure out first is utilization. Everything depends on that. What happened? A team of eight analysts worked on the problem for a week. They suggested clever actions: a promotion here, a discount there, a bundle somewhere else. The director asked, "Great, folks. But what is the utilization?" Answer: "We didn't calculate that." In parallel, AI agents worked on the same task. And the very first thing they did was calculate the utilization. Because that is logically the most important question.

Final Score: AI Agents 1 — Humans 0.

That doesn't mean the agents are superior. Their analysis quality was mediocre. But they asked the right question — and the team overlooked it.

What this story really means

AI today divides all data professionals into two camps, and the line isn't where you might expect. It's not about whether you can use ChatGPT. It's about whether you contribute intellectual added value. Unit 1: Human-Machine Interfaces. These people take business requests and translate them into code. The business has already done all the thinking beforehand — the metric is known, the use case is clear. It just needs to be executed. This role is at high risk. AI is already doing this today, faster and without breaks. Dorm 2: Thinker. These people first ask: Why do we need this number at all? What happens to the result? What are the scenarios? What question are we currently overlooking? This role becomes more valuable—not less.

You have to ask yourself an honest question: Is my work intellectual or quasi-intellectual? If the actual intellectual component was low, then things are bad.

A simple self-analysis: What do you actually do all day?

The practical tool is as simple as it is inconvenient: Sit down for one to two weeks and write down what you do daily. Do you talk to the business? Do you write code? Do you think? Do you design systems? Do you hold meetings? Then, for each item, ask yourself: Can't an AI do this quite well by now? Find your bottleneck. What prevents you from getting faster? Until now, for many, the bottleneck was writing code. Today, this can be solved with AI—you stop getting stuck there. But then comes the serious question: What's left? If the code is gone, what do you do then? If you don't have an honest answer to that—you now know what you need to work on.

The situation by career level: unvarnished

Entry-level professionals: honest words

I'm glad I don't have children aged 18 to 20 who have to enter the workforce now. I have no answer for them.

In large data organizations today, practically no classic junior positions are filled. If interns are hired, they are only those who possess the level of skill that would have been expected of a mid-level employee in the past. About one in a hundred interns meets this new standard. This one person is showered with offers. The other ninety-nine cannot find employment. What distinguishes this one person? They can design a system. They have realized their own projects. They have built something that works. What prevents anyone from simply building a web service, gaining a few hundred users, and working with them? Nothing. But out of a thousand people who understand this, ten to twenty actually do it. It's about something called initiative. Ironically, in the age of AI agents, companies are looking for people with initiative. Drive. The ability to tackle something independently without being told, "Do this." This is not a learnable skill – it's a character trait. And the result is a hard split: a small group is showered with offers, the large majority finds nothing.

Likely hit the hardest

"These people thought they had made it. They are in. They are established. And now the heavy boot of AI is on their fingers."

Mid-level professionals face the most unpleasant task: honestly analyzing their own work. Where do you really spend your time? What of it is valuable? Here's a little-known phenomenon from large tech companies: many engineers don't understand what's truly important. But they know they're being evaluated. So what do they do? The most rational strategy: do as much as possible. The lottery principle – produce a lot in the hope that something valuable emerges. This leads to burnout. And it doesn't lead to better results. The way out: honest self-analysis. Why did I do this? Was it valuable? What really matters? What's holding me back? This is hard. Not everyone can do it. Those who succeed are rewarded. A mentor or external consultant can help here – it's easier to see problems in another person than in oneself.

Senior-Level: Life is getting better

True seniors — those who have always been autonomous units, responsible for a system or process — have it easier today, not harder. Previously, seniors would delegate tasks to junior and mid-level employees — broken down into small pieces, almost like to an agent. Today, those tasks can go to AI agents. The senior's life has become easier. What was previously impossible — for example, being able to test one-twentieth of all hypotheses — is now one-fifth. That's a significant leap. But beware: it is a myth to believe that a senior accomplishes three times as much as a result. A senior's main bottleneck is not their bandwidth — but internal bureaucracy and the absurdity of large corporate processes. AI changes nothing about that.

Leaders: A New, Uncomfortable Question

The task of managers has not changed—they are responsible for results. But a new question arises: If I can get everything I'm expected to do done in 15 hours a week—why should I work the other 25 hours for free?

I work 15 hours a week. The other 25 hours are actually free. Because everything that was expected of me was finished within the first 15 hours. Some people say: No thank you, that's enough for me.

This inevitably leads to the conclusion that performance appraisal systems must be rethought. The old model—"everyone gets more or less the same"—no longer works. Top performers must be visibly rewarded. Major companies are already responding: bonuses up to 300 percent of salary for the best employees. Significantly increased stock packages. And middle management is being drastically reduced. If every senior is a highly productive autonomous unit with agents—why do you need so many managers? Some companies are introducing "Giga-Managers": instead of 5 direct reports, now 10 or 15. No more weekly one-on-one meetings.

What is being asked — and what is becoming a commodity

Here's a clear separation:

Commodity (everyone can do it): Writing code. Still necessary, but no longer distinctive.
Critical (few can do that): Design systems. Understand trade-offs. Keep a whole architecture in mind. Formulate clear thoughts.

The question "which technologies should I learn?" is less important today than it seems:

"Technologies are not crucial. Today one, tomorrow another. You have to be able to solve problems. Build some web service. Get 100 users. Work with it. You'll learn the necessary technologies along the way."

One thing is mandatory: every web frontend for an LLM (chat interface) — that's standard. And an agent-assisted development environment (various tools are available today) — you must master that now. That is not optional.

Where is money actually being made right now – classic ML or GenAI?

An important insight that often gets lost in public debate:

"The vast majority of money is still made with classic machine learning. The entire AI agent economy is based on one idea: We increase productivity. What is productivity increase? It's the ability to do the same with fewer people. The entire GenAI economy is based on us firing all of you."

Concrete success story: A major European fintech company has transitioned three-quarters of its customer support to AI. And the support is actually functioning at a good level – most customers no longer need a human agent. But this is customer support – not the game-changing revolution the market expects. Forecast: In 3 to 5 years, agents will replace ten million jobs in the back offices of large companies – especially in India, Malaysia, and similar hubs. But the return on investment for AI integration in companies is practically zero today, and won't be there next year either.

How to prepare — three steps to your next job

In large data organizations, an application process for senior positions typically looks like this today:

Code Interview. Two formats. Either you get code for review: "What's good, what's bad, what would you change?" Or a code snippet with the task of implementing an ML algorithm. Google and even LLMs are allowed for support—but not for complete writing. You observe how the candidate approaches problems.
System Design. Also two formats. "Greenfield" — design a system from scratch. "Brownfield" — talk about a system you built and how you would change it today.
Behavioral. How do you behave in certain situations? How do you tell about your experience?

The most common mistake candidates make: not thinking through the final answer, but changing it as soon as someone asks, "Is that really your final answer?" The problem isn't a wrong answer. The problem is that the candidate hasn't performed critical self-examination.

How you stand out today

There's a sobering realization: you don't need genius to be in the top five percent. The instructions are mundane: Analyze. Observe how successful people do it. Ask a consultant or mentor if needed. Adapt. Apply. Repeat.

Surprisingly, many people consciously do everything to avoid improving themselves. Even after this explanation, 5 out of 100 will implement it. And if it were 20, the competition would be four times as hard.

Specifically, this means that you can stand out in the market through:

Top-tier data science competitions
Olympics
Meaningful Open-Source Contributions
Publications, Presentations
Own projects with real users

The content isn't crucial. The proof is: this person obsesses over something and sees it through to the end.

Would you enter the profession today?

When asked by an experienced Data Director, this question yields a remarkably honest answer:

"I'm not sure I'd get into data science today, honestly. It's a red ocean. It's the death."

What was different back then? It was easy to get started then, a lot of money, significantly better than the work he had done before. Today, the conditions are different. And where would he go instead? An honest answer: he doesn't know. "The classic problem of a person at the beginning of their career."

Which data field is safest?

Scenario analysis and risk assumption. AI does not take on risk assumption.

Risk is responsibility. And responsibility must be borne by a human — not a machine. Who decides to grant a loan? To pay out an insurance claim? To approve a medication? To make an investment? Who bears the legal, ethical, and personal consequences if the decision was wrong? These questions are not answered by machines. They will not be answered by machines.

Conclusion: It's about new thinking, not new tools

The key takeaways from this market observation:

AI doesn't replace the data expert. It replaces the translator between business and code. Those who do more than that are safer than ever.
Tools are secondary. What library, what framework—it almost doesn't matter. What counts: recognizing problems, designing systems, understanding trade-offs, asking the right questions.
The most important step today: honest self-analysis. What am I really doing? What of it has intellectual added value? What is my bottleneck?
And build something. Not talk, not plan—build. A web service. A small tool. A data analysis with real consequences.

Prove – to yourself first – that you can finish something. In a market that is changing so rapidly, the most valuable signal is not a certificate. It is proof that you can think for yourself. Editorial Note: This article is based on an expert discussion with a long-time executive in the field of data and AI at an international corporation. The assessments and observations presented reflect the personal position of the expert.