Automation index
How much of a Table official’s job can AI already do?
A scoresheet comes earlier. Clock, fouls and not wrecking the result do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Table official
22%
AI can already do 22% of this job.
#346 / 633
View rankingTable official
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 |
|---|---|---|---|
Sheet, clock and the protocol.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Fouls, the warning and the one who argues.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Timeout, the scoreboard and not jumping it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The referee, the table and not contradicting.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The crowd, the shove and keeping calm.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
A clean result, and the sheet signed.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- scoresheets
- clock
- fouls
- timeouts
- scoreboard
- the table
- protocol
- warnings
Sources for this job· O*NET 27-2023.00· ESCO/ISCO 3422
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