Data and analytics

ISCO-08 2120, 2511, 2521

Data analyst, Data scientist, Data engineer, BI developer, Statistician, Database administrator

AI exposure54%

Headcount need, in FTE

Exposure range

Your current headcount

FTE

Need in 203091 FTE-9 FTE (-9%)

Need in 203581 FTE-19 FTE (-19%)

How AI is used on these tasks today

Automation 44%Augmentation 56%

In AI conversations about this job's tasks: the AI does the task itself (automation) or helps the person do it (augmentation).Anthropic Economic Index, April and May 2026. Occupations: Data Scientists, Statisticians. AEI

1. Exposure 54%Average automation potential of the job's tasks. Range for this family: 44 to 65%.ILO occupations used: Mathematicians, Actuaries and Statisticians (56%), Systems Analysts (49%), Database Designers and Administrators (57%). ILO data

2. Strategic ceiling 100%Your call: how much of this potential do you want to capture? Nobody can set it for you.

3. Adoption speed midpoint 2030

Inherits your company stance, adjustable for this family.

4. Conversion to headcount sensitive 35%Default 35%: Business demand for analysis is far from saturated: cheaper analysis means more questions asked, more use cases and wider self-service.

Target AI skills

level 1 to 4

Foundation

Use AI assistants every day

Frame and phrase a request

Check and challenge AI outputs

Protect data and respect the rules

Applied

Rethink one's process with AI

Analyse data with AI

Produce content with AI

Delegate to and supervise AI agents

Expert

Build and integrate AI solutions

Evaluate and make AI solutions reliable

AI governance and risk management

Job skills

Growing in value

Framing business questions with stakeholders
Data quality and semantic layer design
Evaluating models and AI pipelines
Statistical rigour and causal reasoning
Putting AI use cases into production

Losing value

Writing standard SQL queries
Building recurring dashboards and reports
Data cleaning scripts
Ad hoc extractions for business teams
Commentary on descriptive analyses

How the job will change

AI assistants now write SQL, Python and transformation code from a plain request, document pipelines and produce a first exploratory analysis in minutes. Business users can query a well-modelled dataset in natural language, so many ad hoc extraction requests leave the data team's queue. What remains hard is what the assistant cannot see: messy source systems, ambiguous definitions, data that is simply wrong.

The job moves towards data foundations and judgement: reliable pipelines, shared definitions of indicators, a semantic layer that business users and agents can query without error. Data scientists spend more time evaluating AI models and putting them into production than training models from scratch. A good data professional tomorrow knows the business well enough to say when a result is plausible and when it is not.

2026-2027: Assistants write queries and code; self-service analytics spreads to business teams.
2028-2030: Analysis agents answer routine questions; data teams refocus on platforms and quality.
2031+: Fewer report builders, more data product owners and AI evaluators.

Watch out

Senior profiles able to put AI models into production and evaluate them are scarce and courted by every sector. Retaining and upskilling current analysts will matter more than external hiring. See the seniority outlook below.

What if you hired fewer juniors?

Your 2036 seniors are the juniors you hire today.

Seniors available in 2036-10%2 FTE short

Gap above 5% from2031

Your seniority mix today

Juniors (0 to 2 yrs in the profession)35%Seniors (6+ yrs)20%

Mid-level (2 to 6 yrs in the profession, the remainder)45%

Cut in junior hiring -40%

For

Advanced settings

Juniors promoted each year33%Juniors leaving each year15%Senior gap filled by external hiring0%If every company makes the same bet, senior profiles will be scarce and expensive.

Seniority mix, % of today's headcount

Juniors35%Mid-level42%Seniors18%

2026 2036

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How the numbers are built

Every headcount figure combines four assumptions. Three come pre-filled from public research and job family defaults. The strategic ceiling is yours to set.

Public researchExposureAverage automation potential of the job's tasks, from the ILO's 2025 task-level scores.

Your decisionStrategic ceilingHow much of that potential you choose to capture. Your decision, not ours.

Your companyAdoption speedHow fast your company moves, set once for the whole company.

Job family defaultConversion to headcountHow much of the productivity gain becomes fewer people rather than more output.

Headcount effect

Seniority outlook

A flow model with three levels of experience in the profession. Promotion and exit rates are set so that today's mix stays stable when hiring does not change: any gap you see comes from the junior hiring cut alone.