IS WORK OVER?

Automation index

How much of a Data scientist’s job can AI already do?

Today an AI can already classify, reconcile, and prep a first close. The final call, and what goes to the tax office, still has a name on it.

This percentage does not predict how many people will lose their jobs.

Task comparison

Data scientist

Where the time goes

Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.

The time saving for each task is estimated separately.

TaskShareAIPoints
Generate standard or custom reports summarizing business, financial, or economic data for review by executives, managers, clients, and other stakeholders.
Estimate

No public source provides a precise figure. This is our estimate.

14%55%+7.7
Manage timely flow of business intelligence information to users.
Estimate

No public source provides a precise figure. This is our estimate.

13%28%+3.6
Maintain or update business intelligence tools, databases, dashboards, systems, or methods.
Estimate

No public source provides a precise figure. This is our estimate.

11%48%+5.3
Provide technical support for existing reports, dashboards, or other tools.
Estimate

No public source provides a precise figure. This is our estimate.

9%55%+5
Synthesize current business intelligence or trend data to support recommendations for action.
Estimate

No public source provides a precise figure. This is our estimate.

6%28%+1.7
Create business intelligence tools or systems, including design of related databases, spreadsheets, or outputs.
Estimate

No public source provides a precise figure. This is our estimate.

6%55%+3.3
Collect business intelligence data from available industry reports, public information, field reports, or purchased sources.
Estimate

No public source provides a precise figure. This is our estimate.

6%55%+3.3
Identify or monitor current and potential customers, using business intelligence tools.
Estimate

No public source provides a precise figure. This is our estimate.

6%28%+1.7
Other less frequent tasks (weight estimated from frequency).
Estimate

No public source provides a precise figure. This is our estimate.

29%28%+8.1
Total100%40%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • analyse data
  • models
  • dashboards
  • python
  • information structure
  • data warehouse
  • database
  • web programming
  • ICT security legislation
  • business process modelling

Sources for this job· O*NET 15-2051.01· ESCO/ISCO 2521

Olive-oil miller

Where the time goes

Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.

The time saving for each task is estimated separately.

TaskShareAIPoints
Intake, yield and the mill log.
Estimate

No public source provides a precise figure. This is our estimate.

16%40%+6.4
Crush, malaxate and the decanter.
Estimate

No public source provides a precise figure. This is our estimate.

18%12%+2.2
Temperature, time and the fruitiness.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
Cellar, the batch and the one that goes rancid.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
The campaign, the tractor queue and not heating.
Estimate

No public source provides a precise figure. This is our estimate.

18%18%+3.2
Cold for real, and not painting the yield.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%22%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • crush olives
  • malaxation
  • decanter
  • fruitiness
  • the campaign
  • mill yield
  • oil cellar
  • extraction temperature

Sources for this job· O*NET 51-3092.00· ESCO/ISCO 7514

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