Automation index
How much of a Renewables engineer’s job can AI already do?
A solar resource and a layout come earlier. Connecting the plant so it actually exports do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Renewables engineer
27%
AI can already do 27% of this job.
#163 / 623
View rankingRenewables 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 |
|---|---|---|---|
Resource, layout and the yield model.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Site, topography and the cable that will not reach.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Permits, grid point and the TSO.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
EPC, commissioning and export tests.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The neighbour, the council and the appeal against the plant.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it exports, and not inflating equivalent hours.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
- plant layout
- solar resource
- grid connection
- commissioning
- renewables permits
- yield model
- export tests
- EPC
Sources for this job· O*NET 17-2199.00· ESCO/ISCO 2149
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