The Future of the Data Scientist Role in the Age of AutoML and LLMs 2026

January 9, 2026January 9, 2026

Auto ML platforms and large language models (LLMs) are automating a lot of the technical work, so a lot of people are wondering what will happen to the role of Future of the Data Scientist.

The truth in 2025-26 will be that data scientists will not be replaced, but that their work will change. Instead of focusing only on model building, they will also consider product thinking, data quality, and AI governance.

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What kind of tasks are being automated?

Auto ML and LLM powered tools are increasingly automating:

Routine feature engineering and model selection for common tabular problems.
Boilerplate code, SQL generation, and basic EDA.
First drafts of analytics reports, dashboards, and documentation.
This allows for less time to be taken up by the plumbing and repeated modelling, but it still requires understanding the domain, properly framing the questions, and deciding the trade-offs.

How the role is changing:

Data scientists are gradually moving to greater leverage tasks:
Problem framing and experiment design: Developing the business questions into measurable hypotheses, metrics, and experiments.
Data and feature strategy: Deciding what data is to be collected, how to organize it, and how to keep the signal to noise ratio high.
Evaluation and governance: Creating trusted evaluation sets, tracking drift, and making sure that fairness, stability, and compliance stay intact.
AI product thinking: Collaborating with PMs and engineers, not only to build offline models but also to design end, to, end AI features.

There seems to be less differentiation between titles such as “analytics engineer, ” “ML engineer, ” and “applied scientist” in many teams due to the overlapping of the skills in coding, product, and communication.

Skills that are even more important now
If you produce content for current or future data scientists, be sure to emphasize these skills that are in high demand:
Using strong SQL skills and data modelling on the modern data stacks (lake houses, ELT, DBT style workflows).
Ability to manage Auto ML/ LLM tools rather than hand coding everything.
Communication: presenting trade-offs, risks, and results to non-technical stakeholders.
Knowledge of responsible AI, privacy, and data contracts.
There will be a series of posts in which we will present the six, month to one, year upskilling roadmap for the data professionals who want to work with Auto ML and GenAI rather than compete with them.