Automation index
How much of a Youth educator’s job can AI already do?
An incident note comes earlier. Being there at night so nobody absconds do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Youth educator
21%
AI can already do 21% of this job.
#414 / 633
View rankingYouth educator
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 |
|---|---|---|---|
Plan, notes and the young person’s file.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Shift, holding and the attempted abscond.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Workshop, homework and the hour that is not a fight.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Family, youth prosecution and the judge.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Street, leisure and the one who is late back.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
The bond, and not losing the kid.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 21% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- educate young people
- residential shifts
- hold incidents
- incident notes
- education workshop
- youth court
- prevent absconding
- night support
Sources for this job· O*NET 21-1093.00· ESCO/ISCO 2635
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