Automation index
How much of a Hydrologist’s job can AI already do?
A hydrograph comes earlier. Calling the rise without leaving the village blind do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Hydrologist
22%
AI can already do 22% of this job.
#339 / 633
View rankingHydrologist
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 |
|---|---|---|---|
Hydrograph, catchment and the model.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Field, gauging and the one that does not read.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Flood, the alert and not delaying.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Low flow, irrigation and the clash.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The authority, the figure and not dressing the flow.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
If the river rises, call it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- hydrographs
- catchments
- gauging
- floods
- low flow
- hydrology alerts
- flows
- the river authority
Sources for this job· O*NET 19-2043.00· ESCO/ISCO 2114
IS WORK OVER?
ML engineer
29%
#64 / 633
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