Will Data Scientists Be Replaced by AI? Why Demand Is Growing but the Junior Bar Is Rising

TL;DR

• Will data scientists be replaced by AI? No. BLS projects 35% growth from 2025 to 2035, one of the fastest rates of any occupation, and it counts AI adoption as a reason firms need more data scientists.

• The catch is the way in. AI now writes much of the routine code, so junior roles expect statistics, judgment and some engineering on top.

• Data scientists rank among the most AI-exposed occupations in Microsoft's and Anthropic's usage data, which mostly tells you the tools are already part of the job.

• Related roles hold up too: statisticians (+11%) and operations research analysts (+12%) are both projected to beat the average job.

• Your move now: build one project where you design an experiment or evaluate a model, not only train one.

Externships are short, remote professional experience programs where you work on real projects for real companies, with guidance from people in the field. To practice the AI side of data science, look at the Wayfair n8n AI Agent Engineering Externship, the Pfizer Document Insights & Data Extraction Externship or the TruBridge Healthcare Data Analytics Externship. Explore all Externships.

Will AI Replace Data Scientists?

No, and the official numbers point the other way: BLS projects data scientist jobs to grow 35% between 2025 and 2035. Our verdict is Growing with AI, with one big asterisk. The junior bar is rising, because the routine coding that used to train new hires is exactly what AI does well.

The Short Answer

Data scientists aren't being replaced by AI; in most companies they're the people building and checking it. That's why the Bureau of Labor Statistics, on its data scientists page, treats AI as a demand driver: firms keep building AI into how they work, and they need data scientists to make it useful. Not many occupations get that kind of write-up from BLS right now.

In our one-on-one conversations with students, the data science question has changed. It used to be "is this field going to exist?" Now it's closer to "how do I get the first offer when AI can write the code I was going to be hired to write?" That second question is the real one, and most of this guide is about it.

The AI Exposure Scorecard

Every article in this series uses the same five-row scorecard, so fields can be compared side by side. Here's data science.

Wait, "Very high" exposure and 35% growth at the same time? Yes. Exposure measures how much of a job's task list overlaps with what AI can do; it doesn't predict job loss, and BLS says so in its methodology note. In an Extern analysis of BLS's exposure file, occupations in the "Very high" group are projected to grow just 2.0% on average (weighted by employment) from 2025 to 2035. Data scientists sit in that same group and are projected to grow more than fifteen times faster.

So exposure isn't destiny. It tells you the work will change, not whether it disappears. If you're weighing the reporting side of data instead, that answer is different, and we cover it in Will Data Analysts Be Replaced by AI?

What Parts of Data Science Is AI Already Doing?

AI already does a lot of the code-heavy middle of data science: writing SQL to pull data, drafting Python for cleaning and features, fitting baseline models and summarizing results. Framing the question, designing the experiment, judging whether a model is good enough and standing behind the answer are still human work.

Task by Task: What AI Does and What Still Needs You

Here's a typical project broken into pieces. For each one, what AI handles today and what still needs you.

The usage data backs this up. In Anthropic's January 2026 Economic Index report, computer and math tasks (the group that includes data scientists and statisticians) made up about 34% of Claude.ai conversations and 46% of API traffic in November 2025. People are already handing this kind of work to AI at scale.

But read the right-hand column again. Leakage, sample size, metric choice: none of those are typing problems. They're judgment problems, and a model that writes code faster doesn't solve them.

Real Example: The Data Scientists Building the Automation

Here's a detail that says a lot about where the field is going. On the BIRD text-to-SQL benchmark, the top system as of October 22, 2026 is "AskData," submitted by a team in AT&T's Chief Data Office. It writes correct SQL for 83.40% of the benchmark's questions. Human experts score 92.96%.

Two things stand out:

• The people building query automation are data scientists. Somebody has to design these systems, test them and decide when they're reliable enough to trust. That's data science work.

• Even the best system trails humans by about 10 points, on a benchmark with cleaner schemas than most companies have. In a real warehouse with decades of odd table names, the gap is likely wider.

One caution: we found no AT&T announcement describing how, or whether, AskData is used inside the company, so we're only claiming the benchmark result.

And the direction is still clear: the routine query is automating, and the people who can build and evaluate that automation are in demand.

What Does the Job Data Actually Say?

The projections and live postings look strong. The warning signs are in broad hiring indexes and in research on early-career workers, which show a tighter door for people just starting out. Both things are true at once.

The BLS Outlook in Plain Numbers

Beyond that headline growth rate, BLS expects about 24,800 data scientist openings a year through 2035, and the median pay was $120,230 in May 2025. For context, BLS projects all jobs to grow 3.5% over the same decade and the whole computer and math group 7.3%, according to its 2025-35 projections release.

One honest limit: projections are built on assumptions, and BLS adjusts for AI only where it sees convincing evidence. They're the best neutral forecast available. They aren't a guarantee.

From Extern's Data: Data Scientist Postings

Live demand moved up this fall. In US job-posting counts Extern tracks daily (Adzuna, July to October 2026), live "data scientist" postings went from about 21,900 to about 27,000 (monthly averages), a rise of roughly a quarter in three months.

And that lines up with the BLS view, not against it. If you're comparing data science with other majors on pay and AI risk, our ranking of the best paying AI-proof majors puts it in context.

The Warning Signs Worth Taking Seriously

Now the part that's less fun to read.

• A broad postings index is still far below 2020. Indeed's "Mathematics" job postings index on FRED stood at 62.4 on October 23, 2026 (February 2020 = 100). It peaked at 202.6 in March 2022 and bottomed at 58.0 in January 2026. Indeed doesn't publish exactly which titles sit in that category, so treat it as a rough proxy for math and data roles. And the slide started in 2022, before ChatGPT. Some of it is a post-boom correction, not AI.

• Young workers in AI-exposed jobs are falling behind. Stanford's Digital Economy Lab found in August 2026 that employment of 22 to 25-year-olds in the most AI-exposed occupations is about 19% below where it would be had it kept pace with less-exposed peers (ADP payroll data through June 2026). The gap comes mainly from fewer hires, not layoffs, and the authors say their results are descriptive, not proof that AI caused it. The paper doesn't single out data scientists.

• Hiring is tilting senior. Indeed Hiring Lab reported in July 2026 that US entry-level postings were down 7.5% from a year earlier in May 2026, while senior postings rose about 15%.

So, put together: the occupation is growing while the first rung gets harder to reach. Honestly, we don't know yet how much of that squeeze is AI and how much is the post-2022 hiring hangover. Probably both.

Which Data Science Roles Are Safest, and Which Are Most Exposed?

The roles that hold up best are the ones built on judgment: designing studies, formulating problems, evaluating models. The roles most exposed are built on routine output. Here's how data science compares with its closest neighbors.

The pattern: code and routine output are automating; framing problems, judging evidence and owning the decision aren't. Statisticians are a good example. Their observed exposure in Anthropic's data (0.21) is less than half that of data scientists (0.46), partly because so much of the job is deciding what a study can and can't show.

If the reporting-heavy row is closest to what you do now, read Will Data Analysts Be Replaced by AI? for that side.

But if you're torn between data science and software, Will AI Replace Software Engineers? covers a field with a much tougher entry-level picture. For a wider list of careers that hold up, see our guide to jobs AI won't replace.

How Is AI Changing Entry-Level Data Science Work?

Junior data scientists now spend less time writing boilerplate code and more time evaluating, testing and explaining. Employers expect AI tools in your workflow from day one, plus enough statistics to know when their output is wrong.

Then vs Now: A Junior Data Scientist's First Year

Then: a first-year data scientist learned the job by grinding. Write the extraction queries, clean the data, build the baseline model, make the charts, then do it all again next week. Slow? Very. The slowness was the training.

Now: AI drafts most of that in minutes. What's left for the junior is the part that used to come later: checking the AI's query against the source, choosing an evaluation metric that matches the business goal, designing the test, and explaining the result to someone who doesn't care about the code. Anthropic's January 2026 report found that 49% of jobs have seen AI used for at least a quarter of their tasks. For data science, the share is almost certainly higher.

We called this field "Growing with AI," and for the occupation that's right. For one specific new grad with a model-training notebook and nothing else, though, it can feel like the opposite. The growth is real. It's going to people who show they can do the parts AI doesn't.

What Employers Expect on Day One

According to NACE's April 2026 employer survey, nearly 60% of employers now assign interns projects that use AI tools, and 28% say they're specifically seeking early-career talent who can use AI.

And students know they're behind. In Handshake's Class of 2026 report, 58% of seniors said they need stronger AI skills, but only 28% said their program meaningfully built AI into the curriculum.

So where does that leave you? Mostly with a gap you have to close yourself. We'd love to point to a clean number for junior data scientist hiring, but no primary source publishes one. Everything above is broader data that includes data science, not a measure of it alone.

What Skills Make a Data Scientist Hard to Replace?

The skills that hold up are the ones AI can't verify for itself: statistical judgment, model evaluation, clean engineering, data plumbing, building AI tools responsibly and explaining results. Here's each one with a way to prove it, because hiring teams look for evidence, not adjectives.

1. Statistics and experimental design. Proof: an A/B test or causal analysis write-up that shows your sample-size reasoning and states what the result can't tell you.

2. Model evaluation and error analysis. Proof: a project that compares models on the metric the business cares about, with a section on where your best model fails.

3. Clean, reproducible Python. Proof: a repo with tests that anyone can rerun from raw data to final result.

4. Data engineering basics. Proof: a small pipeline (SQL plus a tool like dbt) that turns raw data into model features, with data-quality tests.

5. Building and evaluating AI tools. Proof: a simple retrieval tool or agent with an evaluation set that measures how often it's wrong. That's the same idea as the human-checked reference queries text-to-SQL builders use.

6. Explaining results to decision-makers. Proof: a one-page memo with a recommendation and its risks, written for a real stakeholder.

From the hiring side (our author's background includes years in recruitment process outsourcing), the projects that stand out in a data science screen are the ones with a "what went wrong" section. Plenty of candidates show a model with high accuracy. Far fewer show they caught their own data leakage, and that's the candidate a team trusts with production data.

How Can You Get AI-Ready Data Science Experience as a Student?

Aim for three things before you graduate: at least one internship application cycle, one project with a real company's data and stakeholder, and a clear lane (analyst, scientist or engineer). Here's how each looks in November 2026.

Internships: Where to Look

Two of our hub guides cover the main paths. The data analytics internships guide lists data science programs and timelines by industry, and the AI and machine learning internships guide covers more model-heavy roles. Many summer 2027 programs opened in the fall and review on a rolling basis, so apply now rather than in spring. If e-commerce data interests you, our Wayfair internship guide covers that company's programs.

Externships That Put You on the AI Side of the Table

A class project proves you can train a model. A project for a real company proves you can do it with a stakeholder, unclear requirements and data you didn't pick. Externships run 4 to 11 weeks (most often 8), at about 5 to 10 hours a week, remotely, with an extern manager guiding you. Three open November 2026 cohorts fit data science well:

• The Wayfair n8n AI Agent Engineering Externship, for building AI agents that support business intelligence work.

• The Pfizer Document Insights & Data Extraction Externship, for pulling structured data out of documents with AI, an advanced-level project.

• The TruBridge Healthcare Data Analytics Externship, for analytics on healthcare data.

Artificial intelligence is one of the three fields students most want to explore next in Extern's post-program survey (18.7% of responses, June 2026), alongside data analytics and finance. And skills carry across industries: one student, Erika, completed the TruBridge Externship and went on to an RME planner role at JLL, using what she learned on healthcare data in real estate operations. You can browse current Externships for more options.

Analyst, Scientist or Engineer? Pick a Lane

You don't have to decide forever, but you should decide for your next application cycle. Like explaining numbers to people and working close to business decisions? Analyst. Like statistics, experiments and models? Scientist. Like building the systems that move and clean data? Engineer. Each lane rewards different projects, and a resume that tries to be all three usually reads as none of them.

Extern is not affiliated with, sponsored by or endorsed by any company, firm or organization named here. This article is based on publicly available information as of November 2026. Job data and company plans change, so check the original sources linked above.

Frequently Asked Questions

Will data science be replaced by AI?

No. The field is changing faster than it's shrinking. BLS projects data scientist employment to grow 35% between 2025 and 2035, and it names AI adoption as one reason. What's being replaced is the routine coding inside the job, which raises the bar for juniors instead of removing the role.

Will AI replace data engineers?

Not soon. AI coding tools now write a lot of pipeline and SQL code, but someone still has to own reliability, data quality and what happens when a source system changes overnight. Since wrong AI output reaching decision-makers is a top worry for data teams, the people who keep data trustworthy matter more.

Will AI replace statisticians?

Unlikely. BLS projects statisticians to grow 11% from 2025 to 2035, with median pay of $105,650 in May 2025. AI can run the calculations quickly, but choosing a study design, checking assumptions and saying what the data can't support is the core of the job, and that's judgment work.

Is data science still a good major in 2026?

Yes, if you like statistics and building things, but expect a harder first job search than students had in 2021. In the New York Fed's 2024 data, closely related mathematics majors had 5.8% unemployment. Pair the degree with real projects and at least one internship or company project before you graduate.

What's the difference between a data analyst and a data scientist now?

Analysts mostly answer business questions with existing data, and AI tools in BI software now handle much of their routine reporting. Data scientists build models, design experiments and increasingly build AI tools themselves. Both need SQL and judgment, while scientists need deeper statistics and coding. Our data analysts guide covers the analyst side.

Should data scientists learn to build AI tools?

Yes. Data scientists are often the people who build, test and monitor AI systems inside a company, not only the people using them. One small project, like a retrieval tool or agent with an evaluation set that measures how often it's wrong, shows employers you can work on that side of the table.

Will AI replace data scientists in the next 10 years?

The occupation is projected to keep growing through 2035, so wholesale replacement isn't what the data shows. Expect the job to keep shifting toward problem framing, evaluation and decision support while AI takes on more of the coding. Data scientists who can check and explain AI output will be in the strongest position.