IS WORK OVER?

Automation index

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

A night of telescope time comes earlier. Reducing the data without selling the find early do not.

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

Task comparison

Astronomer

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
Proposal, time and the instrument.
Estimate

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

18%48%+8.6
Observe, calibrate and the one that clouds over.
Estimate

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

16%40%+6.4
Reduce, the pipeline and the artefact.
Estimate

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

16%22%+3.5
Paper, the referee and not jumping.
Estimate

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

16%28%+4.5
The facility, the queue and not losing the night.
Estimate

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

18%14%+2.5
Reduce, and not selling the find early.
Estimate

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

16%12%+1.9
Total100%27%

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

Skills and activities

  • telescope time
  • calibrate
  • pipelines
  • papers
  • artefacts
  • nights
  • proposals
  • instruments

Sources for this job· O*NET 19-2011.00· ESCO/ISCO 2111

Data architect

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
Models, lineage and the catalogue.
Estimate

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

18%52%+9.4
Pipelines, quality and the data contract.
Estimate

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

16%40%+6.4
The analyst who finds nothing and the mess.
Estimate

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

16%22%+3.5
Privacy, retention and the field you cannot keep.
Estimate

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

16%28%+4.5
The business, shadow IT and not building another lake.
Estimate

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

18%14%+2.5
That it gets used, and not letting raw data rot.
Estimate

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

16%12%+1.9
Total100%28%

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

Skills and activities

  • model data
  • lineage
  • data catalogue
  • data contracts
  • pipeline quality
  • retention
  • data lakes
  • privacy

Sources for this job· O*NET 15-1243.00· ESCO/ISCO 2511

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