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

Tram driver

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
Checks, log and the cab.
Estimate

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

16%32%+5.1
Drive, stops and the pedestrian who steps out.
Estimate

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

24%8%+1.9
Badly parked car, calls and Control.
Estimate

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

16%22%+3.5
Timetable, reliefs and rush hour.
Estimate

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

16%28%+4.5
The tourist, the bike and not over-hooting.
Estimate

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

14%18%+2.5
Hit nobody, and make the terminus.
Estimate

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

14%12%+1.7
Total100%19%

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

Skills and activities

  • drive trams
  • urban stops
  • pedestrians
  • badly parked cars
  • Control
  • rush hour
  • cab checks
  • terminus

Sources for this job· O*NET 53-4041.00· ESCO/ISCO 8311

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