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Professionals · code 2120

Mathematicians, actuaries and statisticians

AI can run the analysis. Signing off on whether it's right is still an actuary.

AI could take a fair chunk of nine of the ten tasks, from designing surveys and pension schemes to writing up the findings. Only supervising assistants is mostly out of its reach.

What they actually do (and what else they get called)

Mathematicians, actuaries and statisticians conduct research and improve or develop mathematical, actuarial and statistical concepts, theories and operational methods and techniques and advise on or engage in their practical application in such fields as engineering, business and social and other sciences. Tasks include - (a) studying, improving and developing mathematical, actuarial and statistical theories and techniques; (b) advising on or applying mathematical principles, models and techniques to a wide range of tasks in the fields of engineering, natural, social or life sciences; (c) conducting logical analyses of management problems, especially in terms of input-output effectiveness, and formulating mathematical models of each problem usually for programming and solution by computer; (d) designing and putting into operation pension schemes and life, health, social and other types of insurance systems; (e) applying mathematics, statistics, probability and risk theory to assess potential financial impacts of future events; (f) planning and organizing surveys and other statistical collections, and designing questionnaires; (g) evaluating, processing, analysing and interpreting statistical data and preparing them for publication; (h) advising on or applying various data collection methods and statistical methods and techniques, and determining reliability of findings, especially in such fields as business or medicine as well as in other areas of natural, social or life sciences; (i) preparing scientific papers and reports; (j) supervising the work of mathematical, actuarial and statistical assistants and statistical clerks. Examples of the occupations classified here: - Actuary - Demographer - Mathematician - Operations research analyst - Statistician Some related occupations classified elsewhere: - Information systems analyst - 2511 - Actuarial assistant - 3314 - Mathematical assistant - 3314 - Statistical assistant - 3314 - Actuarial clerk - 4312 - Statistical clerk - 4312

  • demographer
  • gambling, betting, and lottery game developer
  • mathematician
  • gambling, betting, and lottery quality assurance engineer
  • actuarial consultant
  • statistician

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

44 / 100

Changing significantly

AI could do a real share of the everyday tasks. The job is changing shape, not vanishing.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Exposed: Gradient 3

401 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 10 tasks for how much of it AI could do. They average 56.2 out of 100, so it holds up at 100 − 56.2 = 43.8.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Exposed: Gradient 3”: Most of the tasks are affected.

This job rates 7.2 out of 100 for how hands-on it is. The score above only looks at AI tools, not robots, but for work this far from the physical that matters less.

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Man v Bot · compare

Who holds up better?

Mathematicians, actuaries and statisticians 44

v

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

  • Data analysis.
  • Statistical and actuarial models.
  • Reports.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Judging whether results make sense.
  • Professional sign-off.
  • Advising on risk.

Both

What's arriving

New work that only exists because the AI does.

  • Checking AI models.
  • Climate risk modelling.
  • Data science roles.

Written by a human, from the ILO's task ratings below. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: Medium

58 could do without you

It could take over some of the work, and help with more. It could help with 77 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Lower

7 how hands-on

Not much of this job needs hands, so the robot can sit this one out.

So could a robot do the physical part?

Less than half of this job is hands-on. Robots still matter, but for a smaller part of the work, so we don't sort it any further.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 4 out of 100. The higher it is, the longer the robot waits.

7 How
hands-on
4 How fiddly
and cramped

Kind of hands-on work Less than half hands-on

The small print

They aren't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. These are the closest thing we have: how hands-on the job is, and how much fiddly, cramped work it takes, which is what Frey and Osborne found hardest for machines.

From O*NET, averaged across 6 US jobs .

finger skill 9.8 · hand skill 1.4 · cramped spaces 2 · averaged · rule: hands-on under 50

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

The job, task by task

Every job is really a bundle of tasks. Here's this one unpacked, with how much of each AI could do, out of 100. The score is the average, and averages can hide a lot.

AI's favourite task here scores 67, against an average of 56. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 67

    Planning and organizing surveys and other statistical collections, and designing questionnaires

    Medium
  2. 63

    Designing and putting into operation pension schemes and life, health, social and other types of insurance systems

    Medium
  3. 63

    Evaluating, processing, analysing, and interpreting statistical data and preparing them for publication

    Medium
  4. 58

    Advising on or applying various data collection methods and statistical methods and techniques, and determining reliability of findings, especially in such fields as business or medicine as well as in other areas of natural, social or life sciences

    Medium
  5. 57

    Applying mathematics, statistics, probability and risk theory to assess potential financial impacts of future events

    Medium
  6. 56

    Advising on or applying mathematical principles, models and techniques to a wide range of tasks in the fields of engineering, natural, social or life sciences

    Medium
  7. 55

    Conducting logical analyses of management problems, especially in terms of input-output effectiveness, and formulating mathematical models of each problem usually for programmeming and solution by computer

    Medium
  8. 54

    Studying, improving and developing mathematical, actuarial and statistical theories and techniques

    Medium
  9. 53

    Preparing scientific papers and reports

    Medium
  10. 35

    Supervising the work of mathematical, actuarial and statistical assistants and statistical clerks.

    Low

10 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Sidekick or replacement?

A tool that makes you faster is a very different thing from one that does the task while you watch. Jobs and Skills Australia measured the two separately, which is handy.

AI helps you
77
AI does it for you
58

Sidekick wins, by 19 points.

How good a match is this?

One Australian job matches, but only partly: not everyone in it does this job. Treat the numbers as a rough guide.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 2241

Meanwhile, in real life…

Everything else here is about what AI could do. This is what people actually did. When they asked Claude for help with this kind of work, did they hand the whole task over, or roll their sleeves up and work through it together?

Out of every 100 conversations

54
46

worked through it together handed it over

The small print

Anthropic publishes this about its own product, and it doesn't feed into any score here. These are people who use Claude, not workers in general, so little use here doesn't mean AI can't reach the job. This job makes up 1.87% of the conversations.

The data is sorted by US job, and this one matches 6 of them. We've combined them, counting each by how often it comes up in the conversations, so the busiest one counts most.

These two add up to 100. The bars elsewhere on the page don't, because each is scored on its own, so we don't put them side by side.

observed use Anthropic Economic Index release 2026-06-26, period beginning 2026-05-01 data 0.8-observed-use 2026-05-01 · Anthropic, about its own product

So, should statisticians and actuaries be worried?

Short answer: not worried, but paying attention. The job is changing.

Parts of the job are exposed. Surveys, pension and insurance design, data analysis, methods, risk, models, theory and papers are rated Medium; supervision is Low. AI takes on analysis and coding; professional judgement stays human.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing.

Where this job could lead

How are the neighbours doing?

There are 92 jobs in the “Professionals” family, averaging 62. This one is below the family average.

Pit this against any other job →