Automation index
How much of a Environmental engineer’s job can AI already do?
A discharge model comes earlier. A plant that complies and does not poison the river does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Environmental engineer
25%
AI can already do 25% of this job.
#229 / 623
View rankingEnvironmental 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 |
|---|---|---|---|
Model emissions, discharges and noise.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Impact studies, permits and specs.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Field, sampling and treatment plants.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Authorities, public and objections.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Rules, reports and monitoring.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Not dressing an environmental figure.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Model emissions, discharges and noise
- Impact studies, permits and specs
- Field, sampling and treatment plants
- Authorities, public and objections
- Rules, reports and monitoring
- Not dressing an environmental figure
- ingeniero ambiental
- environmental engineer
Sources for this job· O*NET 17-2081.00· ESCO/ISCO 2143
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