Automation index
How much of a Tactical analyst’s job can AI already do?
A clip comes earlier. The manager using it, and it not being noise, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Tactical analyst
27%
AI can already do 27% of this job.
#132 / 623
View rankingTactical analyst
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 |
|---|---|---|---|
Video, tags and the report.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Opponent, patterns and the one who hides.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Preview, the dressing room and not drowning them.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Live, the tablet and the change.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The manager, the ego and not contradicting in public.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it is used, and not being noise.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
- tag video
- opponent patterns
- preview
- bench tablet
- tactical reports
- dressing room
- changes
- data noise
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