Automation index
How much of a Talent scout’s job can AI already do?
A stock report comes earlier. Seeing the player without falling for the goal do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Talent scout
27%
AI can already do 27% of this job.
#170 / 623
View rankingTalent scout
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 |
|---|---|---|---|
Match, notes and the report.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
See them live, the setting and the one who hides.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Data, GPS and not trusting the clip alone.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Network, agents and the one already promised.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The club, the rush and not inflating the average.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
See the player, and not falling for the goal.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
- scout reports
- see live
- player GPS
- agents
- scout network
- data
- clips
- the player’s setting
Sources for this job· O*NET 27-2022.00· ESCO/ISCO 3422
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