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
IS WORK OVER?
Astronomer
27%
AI can already do 27% of this job.
#134 / 623
View rankingAstronomer
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.
| Task | Share | AI | Points |
|---|---|---|---|
Proposal, time and the instrument.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Observe, calibrate and the one that clouds over.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Reduce, the pipeline and the artefact.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Paper, the referee and not jumping.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The facility, the queue and not losing the night.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Reduce, and not selling the find early.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 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
IS WORK OVER?
ML engineer
29%
#64 / 623
View rankingML engineer
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.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511