Automation index
How much of a Battery technician’s job can AI already do?
A BMS comes earlier. Diagnosing the module without setting the bank on fire do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Battery technician
19%
AI can already do 19% of this job.
#547 / 643
View rankingBattery technician
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 |
|---|---|---|---|
BMS, SOH and the log.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Module, the imbalance and the one that swells.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Shop, isolation and not shorting.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Thermal, the fire and the protocol.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The vehicle, the warranty and not swapping blind.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
No fire, and the module right.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- BMS
- SOH
- modules
- imbalance
- swelling
- isolation
- thermal fire
- battery warranty
Sources for this job· O*NET 17-3023.00· ESCO/ISCO 3113
IS WORK OVER?
ML engineer
29%
#64 / 643
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