Automation index
How much of a Hydraulic engineer’s job can AI already do?
A floodplain model comes earlier. Sizing the channel so the village does not flood do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Hydraulic engineer
27%
AI can already do 27% of this job.
#164 / 623
View rankingHydraulic 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 |
|---|---|---|---|
Hydrology, flows and the floodplain model.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Field, section and what LiDAR misses.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Drainage works, pumps and the outfall.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Water permits and the river authority.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The flood, the village and the yes to the channel.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it does not flood, and not trusting the model alone.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
- model floodplains
- flows
- drainage
- channel works
- water permits
- pumps
- channel sections
- river authority
Sources for this job· O*NET 17-2051.00· ESCO/ISCO 2142
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