Yes—but the job is changing. Automation can handle or accelerate parts of data science, especially repetitive modeling work. People are still needed to define useful questions, understand messy real-world context, test whether results are credible, make responsible decisions, and explain implications to others. In the United States, the Bureau of Labor Statistics (BLS) projects strong growth for data-scientist employment through 2035, while stressing that projections are conditional rather than guarantees.
What data scientists do beyond writing code
Data science is a lifecycle, not a single modeling task. Depending on the organization, a data scientist may:
- Gather, interpret, clean, and engineer data from multiple sources.
- Explore patterns, measure uncertainty, and identify limitations or bias.
- Choose, train, and evaluate statistical or machine-learning models.
- Translate findings into forecasts, experiments, recommendations, or decision-support tools.
- Work with subject-matter experts and communicate what the evidence does—and does not—justify.
Automation can reduce effort in individual stages, but it does not automatically decide which problem matters, whether a metric reflects the organization’s real goal, or how a result should affect people.
How automation changes the work
Modeling is increasingly automatable
AutoML and related tools can automate portions of feature processing, model selection, hyperparameter search, and evaluation. The 2022 paper Automating Data Science: Prospects and Challenges, published in Communications of the ACM, describes automation as especially advanced in modeling.
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Open-ended judgment remains difficult to automate
The same paper says open-ended, context-dependent work is harder to automate because it requires human interaction. That includes deciding what a prediction should optimize, recognizing when training data do not represent current conditions, balancing competing risks, and obtaining agreement from the people who will use the result. Its conclusion is that “Automation in data science aims to facilitate and transform the work of data scientists, not to replace them.”
Automation preferences differ by task and role
A 2021 study of 217 data-science and machine-learning workers found that desired automation levels and explanation needs varied by lifecycle stage and by worker role. The study argued that practitioner needs did not support complete end-to-end automation. This is evidence about how workers want tools to behave, not a forecast of employment numbers.
What the U.S. employment outlook says
The BLS Occupational Outlook Handbook, using its 2025–35 projections and updated in 2026, reports the following for data scientists:
| Measure | BLS figure | How to read it |
|---|---|---|
| Employment in 2025 | 275,600 jobs | Estimated U.S. occupational employment at the starting point of the projection. |
| Projected employment in 2035 | 371,000 jobs | Employment level under BLS assumptions, not a promise of actual future results. |
| Projected growth, 2025–35 | 35% | Compared with 3% projected growth for all U.S. occupations over the same period. |
| Average annual openings, 2025–35 | About 24,800 | Includes openings caused by occupational transfers and people leaving the labor force, including retirement; not every opening is a newly created job. |
These figures describe the United States and the BLS data-scientist occupation as a whole. They do not establish demand in another country, guarantee employment for a particular applicant, or show outcomes by specialty, seniority, employer, or state.
“The projections are not intended to be a forecast of what the future will be but instead are a description of what would be expected to happen under these specific assumptions and circumstances.”
— U.S. Bureau of Labor Statistics, explanation of its 2026 employment projections
BLS also notes that AI’s labor-market effects are highly uncertain and cannot be predicted precisely a decade ahead. Treat 35% as a conditional planning estimate: actual employment can differ if economic, technological, or other assumptions change.
Why human capabilities still matter
Problem framing and domain context
A model can optimize a target supplied by a person. It cannot, by itself, determine whether that target represents the organization’s actual objective or whether a short-term improvement creates a longer-term problem.
Critical evaluation
People must examine data quality, sampling, leakage, shifting conditions, uncertainty, fairness, and operational constraints. Automated output still needs independent validation before it guides a consequential decision.
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Communication and coordination
Data scientists often explain assumptions and trade-offs to non-specialists, negotiate definitions with stakeholders, and make recommendations that others can act on. BLS identifies communication, logical thinking, and mathematics among relevant skills.
Responsible use of AI
The International Labour Organization’s 2026 analysis says AI adoption is increasing the importance of higher-order cognitive and socioemotional skills alongside digital, data-science, and AI skills. It highlights AI literacy, adaptability, resilience, and human agency. These capabilities help workers supervise automated systems rather than accept their outputs uncritically.
What this means for someone considering data science
Build technical foundations
BLS says data scientists typically need at least a bachelor’s degree in mathematics, statistics, computer science, or a related field; some employers require or prefer graduate degrees. A practical foundation includes probability and statistics, programming, data management, experiment design, and model evaluation.
Practice the work automation cannot fully specify
- Turn an ambiguous business or public problem into a measurable question.
- Choose metrics that reflect real-world costs and benefits.
- Investigate missing, biased, or changing data.
- Explain uncertainty and limitations in plain language.
- Document decisions and monitor systems after deployment.
Learn to use automation as an assistant
Use automated tools to generate baselines, test alternatives, and reduce repetitive work, then review their assumptions and outputs. The valuable skill is not avoiding automation; it is knowing when its result is unsuitable and being able to correct or replace it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is data science a secure career?
No occupation-wide projection can guarantee security for an individual. The BLS growth estimate is national, covers 2025–35, and depends on stated assumptions. Employers may change hiring standards, combine data-science duties with other roles, or automate particular tasks faster than expected. A stronger position comes from combining statistical and computing ability with domain knowledge, communication, critical judgment, and AI literacy.
Is the need global?
The 35% growth and 24,800 annual-openings figures are U.S. estimates. The ILO’s 2026 report provides a broader discussion of changing workplace skills, but it does not provide comparable country-by-country projections for data-scientist employment. Therefore, the evidence supports continued need in the United States and a broader skills trend, not a single worldwide jobs forecast.
The practical answer
Automation is likely to remove or reshape some data-science tasks, particularly routine technical steps. It does not remove the need to connect evidence with human goals, constraints, and consequences. Organizations still need people who can define worthwhile questions, supervise automated analysis, judge whether results make sense in context, and communicate decisions responsibly. That is why data scientists remain relevant even as the tools they use become more automated.
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