Automation index
How much of a Strength and conditioning coach’s job can AI already do?
A load GPS comes earlier. Getting them ready without breaking them in the gym do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Strength and conditioning coach
22%
AI can already do 22% of this job.
#365 / 633
View rankingStrength and conditioning coach
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 |
|---|---|---|---|
Load, GPS and the microcycle.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Gym, the pattern and the one who cheats.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Rehab, the injured and not rushing the return.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Report, the manager and the fight over minutes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The peak, pre-season and not grinding them.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Ready, and not broken.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
- load
- training GPS
- microcycles
- rehab
- movement pattern
- pre-season
- reports to the manager
- the gym
Sources for this job· O*NET 29-9091.00· ESCO/ISCO 3423
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