Somewhat Resilient
Last Update: 6/19/2026
AI Resilience Score for Statisticians:
49.1%
Median Score
Meaningful human contribution
Measures the parts of the occupation that still require a human touch. This score averages data from up to four AI exposure datasets, focusing on the role’s resilience against automation.
Low
Long-term employer demand
Predicts the health of the job market for this role through 2034. Using Bureau of Labor Statistics data, it balances projected annual job openings (60%) with overall employment growth (40%).
Med
Sustained economic opportunity
Measures future earning potential and career flexibility. This score is a blend of total projected labor income (67%) and the role’s inherent ability to adapt to economic and technological shifts (33%).
High
This reflects the reliability of your score based on the number of data sources available for this career and how closely those sources agree on the outlook. A higher confidence means more consistent evidence from labor experts and AI models.
Most data sources align, with only minor variation. This is a well-supported result.
Contributing sources
AI Resilience Report forStatisticians
$103,300 median salary•2,000 annual openings•SOC Code: 15-2041.00
Statisticians are somewhat less resilient to AI impacts than most occupations, according to our analysis of 7 sources.
Statisticians are labeled "Somewhat Resilient" because AI is actively changing how they work, even if it is not replacing them outright. Routine tasks like cleaning data, running models, and building charts are increasingly handled by AI tools, which means the day-to-day job is shifting pretty significantly toward higher-level work like designing studies, interpreting results, and advising decision-makers.
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View analysisChat with CoachLatest newsMore career infoAnalysisChatNewsMoreThis role is somewhat resilient
Statisticians are labeled "Somewhat Resilient" because AI is actively changing how they work, even if it is not replacing them outright. Routine tasks like cleaning data, running models, and building charts are increasingly handled by AI tools, which means the day-to-day job is shifting pretty significantly toward higher-level work like designing studies, interpreting results, and advising decision-makers.
Read full analysisAnalysis of Current AI Resilience
Statisticians
Updated Quarterly
How is AI changing Statisticians jobs?
Right now, AI is mostly augmenting statisticians rather than replacing them — and that's actually good news. The Royal Statistical Society points out that AI systems themselves are fundamentally statistical [1], meaning they rely on the same pattern-recognition principles statisticians have used for decades, which makes statisticians essential for building, evaluating, and governing these tools. The most automated tasks are the routine ones: cleaning datasets, running models, and generating charts.
In pharma, for example, an ASA Biopharmaceutical Report perspective [2] describes how generative AI knowledge-management systems are cutting report preparation time and helping statisticians shift from "data analyst" work into strategic partner roles. Higher-level tasks — designing experiments, presenting findings, supervising data collection, and publishing peer-reviewed research — still depend on human judgment. As Brookings notes, technologies that augment rather than automate work tend to drive job growth [3], which lines up with what's happening here.
Sources
How fast is AI adoption growing for Statisticians?
Adoption is moving quickly because statistical software is one of the easiest places to plug AI in — coding assistants, auto-EDA tools, and LLM-powered report writers are widely available and cheap compared to a statistician's salary. The World Economic Forum highlights that the real payoff comes from redesigning workflows around human-AI collaboration [4], not pure automation. Demand is still strong: the Bureau of Labor Statistics projects 8% growth for mathematicians and statisticians from 2024–2034, much faster than average [5], and the broader BLS Monthly Labor Review notes that data-focused roles are expected to expand substantially [5] as organizations build out AI capabilities.
Adoption could slow in regulated areas like clinical trials or official statistics, where accuracy, bias, and explainability matter — and that's exactly where human statisticians remain irreplaceable.
Sources
Will AI replace Statisticians?
Not entirely. We think AI will take over some tasks, but not the whole job.
Statisticians earn a 49.1% AI Resilience Score from us, which reflects real pressure without signaling a profession in freefall. The routine work, cleaning data, running standard models, generating charts, is already being handed off to AI tools. That shift is real and it's happening fast, partly because statistical software is one of the easiest places to plug in automation.
What stays human is the harder, higher-stakes stuff: designing experiments, interpreting results for non-technical audiences, catching bias, and making judgment calls in regulated fields like clinical trials where accuracy and explainability are non-negotiable. The Royal Statistical Society points out that AI systems are fundamentally statistical at their core [1], which actually keeps statisticians in the room as the people who build, evaluate, and govern these tools. The Bureau of Labor Statistics projects 8% growth for mathematicians and statisticians through 2034, faster than average [5], and Brookings notes that technologies which augment rather than automate work tend to drive job growth [3].
The honest picture is that the role is evolving more than it is disappearing. Statisticians who lean into that shift, treating AI as a collaborator rather than a threat, are likely to find more interesting work, not less of it.
Sources
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Latest AI news for Statisticians
These articles highlight the evolving landscape for statisticians in an AI-driven world. For instance, the study on embracing AI in the labor market emphasizes how statisticians can adapt and thrive by leveraging their skills in data analysis, making them invaluable in scrutinizing AI models. Additionally, the UNECE survey reveals that generative AI is already reshaping statistical organizations, showcasing opportunities for statisticians to lead in implementing innovative solutions. This reflects a hopeful outlook on AI resilience, suggesting that statisticians will remain essential in navigating and enhancing AI applications.
The Real Reason AI Doesn’t Show Up In The GDP Statistics
www.forbes.com • 6/6/2026
Major technological revolutions often prompt doubts about whether existing economic measures can keep up. Artificial intelligence is no...
Bipartisan Senators Seek Data on Potential AI Job Losses
www.meritalk.com • 3/10/2026
A bipartisan group of senators is calling on the U.S. government to expand data collection on the impact of artificial intelligence (AI) on...
Generative AI already having significant impact in Statistical Organizations, reveals UNECE survey
unece.org • 9/9/2024
Results of an international survey conducted by the UNECE Conference of European Statisticians (CES) to explore the use of generative AI...
Embracing artificial intelligence in the labour market: the case of statistics
www.nature.com • 8/30/2024
This study delves into the evolving role and resilience of these disciplines within the AI-influenced labour market. Focusing on statistics as a representative...
AI and Statistics: Perfect Together
sloanreview.mit.edu • 4/16/2024
Business leaders can identify and avoid flawed AI models by employing statistical methods and statistics experts.
More Career Info
Career: Statisticians
They analyze numbers and data to help solve problems and make decisions in fields like business, health, and science.
Parent Careers
Employment & Wage Data
Median Wage
$103,300
Jobs (2024)
32,200
Growth (2024-34)
+8.5%
Annual Openings
2,000
Education
Master's degree
Experience
None
Source: Bureau of Labor Statistics, Employment Projections 2024-2034
Task-Level AI Resilience Scores
AI-generated estimates of task resilience over the next 3 years
1
Report results of statistical analyses in peer-reviewed papers and technical manuals.
2
Supervise and provide instructions for workers collecting and tabulating data.
3
Present statistical and nonstatistical results using charts, bullets, and graphs in meetings or conferences to audiences such as clients, peers, and students.
4
Develop an understanding of fields to which statistical methods are to be applied to determine whether methods and results are appropriate.
5
Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.
6
Design research projects that apply valid scientific techniques and use information obtained from baselines or historical data to structure uncompromised and efficient analyses.
7
Develop and test experimental designs, sampling techniques, and analytical methods.
Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.
