Automation index
How much of a Job insertion technician’s job can AI already do?
An agreement and a card come earlier. Placing someone nobody calls, and them lasting the month, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Job insertion technician
24%
AI can already do 24% of this job.
#282 / 633
View rankingJob insertion technician
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 |
|---|---|---|---|
Employer prospecting and the agreements.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Go with them to the job and the first day.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Follow-up, incidents and the one who quits.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Reports, indicators and justifying the grant.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The hard candidate and the employer who pulls out.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That they stay, and not dressing the figure.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- prospect employers
- place candidates
- follow up in post
- insertion agreements
- justify grants
- job accompaniment
- workplace incidents
- insertion indicators
Sources for this job· O*NET 21-1093.00· ESCO/ISCO 2423
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