Somewhat Resilient
Last Update: 7/31/2026
AI Resilience Score for Statisticians:
48.5%
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
$105,650 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 8 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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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 infoAnalysisChatNewsMoreAnalysis 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 48.5% AI Resilience Score from us, which puts them in a real zone of change. The routine work, cleaning datasets, running standard models, generating charts, is already being automated or accelerated by coding assistants and AI-powered reporting tools. That shift is real and it is happening now.
What stays human is the harder, higher-stakes work. Designing experiments, catching bias, explaining findings to decision-makers, and signing off on results in regulated fields like clinical trials all require judgment that AI cannot reliably provide. AI systems are themselves fundamentally statistical [1], which actually makes statisticians more valuable for building and auditing them, not less. In pharma, for instance, generative AI is cutting report prep time and pushing statisticians into more strategic roles [2], which is a shift in what the job looks like, not an elimination of it.
The economic picture holds up reasonably well. The Bureau of Labor Statistics projects 8% growth for mathematicians and statisticians through 2034 [5], and technologies that augment rather than automate work tend to drive job growth over time [3]. The job will evolve, but it is not going away.
Sources
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Latest AI news for Statisticians
These articles highlight the growing influence of AI on statistics careers, showcasing both challenges and opportunities. The UNECE survey indicates that generative AI is already reshaping statistical organizations, emphasizing the need for statisticians to adapt and incorporate these technologies. Additionally, the study from Nature explores how statisticians can remain resilient in an AI-driven labor market, suggesting that those who embrace AI will thrive. Lastly, innovations like AI disproving longstanding statistical conjectures illustrate the potential for statisticians to engage with cutting-edge developments, making their roles even more vital.
GPT-5.6 AI disproves 20-year statistics conjecture with proof
www.msn.com • 7/16/2026
AI cracks statistics puzzle: GPT-5.6 Sol Pro found a counterexample disproving a 20-year-old conjecture on the Benjamini-Hochberg procedure...
Using advanced statistics and AI to improve health
hsph.harvard.edu • 3/31/2026
Matlin Gilman, PhD '26, studies the real-world health effects of policy decisions and builds artificial intelligence tools to advance health...
Potential occupational exposure to artificial intelligence and automation among certified journeypersons in Canada
www150.statcan.gc.ca • 1/28/2026
Artificial intelligence (AI) and automation hold the potential to transform the nature of work, raising concerns about how different...
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...
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
$105,650
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.
