Automation index
How much of a Zookeeper’s job can AI already do?
An enclosure protocol comes earlier. Going in, feeding and not losing an animal do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Zookeeper
19%
AI can already do 19% of this job.
#489 / 633
View rankingZookeeper
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 |
|---|---|---|---|
Protocol, ration and the enclosure log.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Go in, clean and the one that will not come off.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Enrichment, the public and not teaching badly.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Vet, the sign and calling it in time.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Escape, the protocol and not improvising.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Not losing an animal, and not skipping the lock.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
- enclosures
- zoo rations
- enrichment
- escape protocol
- vet log
- lock-up
- enclosure clean
- the public
Sources for this job· O*NET 39-2021.00· ESCO/ISCO 5164
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