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Technology & Data · Updated Aug 2026

Statisticians

Develop or apply mathematical or statistical theory and methods to collect, organize, interpret, and summarize numerical data to provide usable information. May specialize in fields such as biostatistics, agricultural statistics, business statistics, or economic statistics. Includes mathematical and survey statisticians.

JVS 2.0.0-phase4b
AI Exposure
77/100
Very high

How much of this occupation’s work can be materially affected by current AI systems.

Replacement Risk
73/100
High

How likely exposure is to translate into reduced human demand.

Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured

Evidence quality
Confidence88/100
Task coverage98%

Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.

Task-level evidence

What is driving the score?

Occupation scores are built from the task mix—not a single prediction about a job title.

JVS 2.0.0-phase4b
TaskImportanceAI impactExposure
Analyze and interpret statistical data to identify significant differences in relationships among sources of information.High
80
Report results of statistical analyses, including information in the form of graphs, charts, and tables.High
81
Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.High
81
Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.High
80
Identify relationships and trends in data, as well as any factors that could affect the results of research.High
79
Determine whether statistical methods are appropriate, based on user needs or research questions of interest.High
80
Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering.High
80
Process large amounts of data for statistical modeling and graphic analysis, using computers.High
80
Develop and test experimental designs, sampling techniques, and analytical methods.High
81
Evaluate sources of information to determine any limitations, in terms of reliability or usability.High
79
Design research projects that apply valid scientific techniques, and use information obtained from baselines or historical data to structure uncompromised and efficient analyses.High
80
Develop software applications or programming for statistical modeling and graphic analysis.Medium
79
Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students.High
57
Apply sampling techniques, or use complete enumeration bases to determine and define groups to be surveyed.Medium
80
Report results of statistical analyses in peer-reviewed papers and technical manuals.Medium
81
Plan data collection methods for specific projects, and determine the types and sizes of sample groups to be used.Medium
63
Supervise and provide instructions for workers collecting and tabulating data.Medium
80
Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.Medium
33
Most exposed

Where AI can do more

Routine, digitized, and highly repeatable tasks face the greatest pressure.

  1. Report results of statistical analyses, including information in the form of graphs, charts, and tables.81
  2. Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.81
  3. Develop and test experimental designs, sampling techniques, and analytical methods.81
  4. Report results of statistical analyses in peer-reviewed papers and technical manuals.81
Hardest to automate

Where people still matter

These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.

  1. Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.01
  2. Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students.02
  3. Plan data collection methods for specific projects, and determine the types and sizes of sample groups to be used.03
  4. Analyze and interpret statistical data to identify significant differences in relationships among sources of information.04
  5. Identify relationships and trends in data, as well as any factors that could affect the results of research.05
Where else this work leads

Related occupations

Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.

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Beyond AI capability

Adoption and labour-market outlook

Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.

Human dependency53
Physical dependency16
Adoption pressure66
Labour-market resilience47
Methodology & sources

O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.

Confidence88/100
CalculatedAug 21, 2026
Read methodology →