Automation index
How much of a Career counsellor’s job can AI already do?
A test and a pathway come earlier. Getting that person to the interview do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Career counsellor
29%
AI can already do 29% of this job.
#66 / 623
View rankingCareer counsellor
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 |
|---|---|---|---|
Profile, test and the map of possible jobs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
CV, letter and the mock interview.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The session, the block and the one who believes nothing.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Employers, placements and the bridge to a contract.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Public employment service, training and the calls.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
That they show up, and not dropping the case.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- career guidance
- write CVs
- mock interviews
- job pathway
- refer to training
- broker employers
- employability tests
- accompany to interviews
Sources for this job· O*NET 21-1012.00· ESCO/ISCO 2423
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