Automation index
How much of a Taxidermist’s job can AI already do?
A form comes earlier. Mounting the animal without smell or hair slip do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Taxidermist
21%
AI can already do 21% of this job.
#438 / 633
View rankingTaxidermist
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 |
|---|---|---|---|
Skin, tan and the form.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Mount, set the eyes and the expression.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Drying, pests and the hair that slips.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Client, the trophy and what you cannot do.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
CITES, the permit and not mounting the illegal.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
No smell, and the hair in place.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 21% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- tan skins
- mount trophies
- forms
- CITES
- hair
- expression
- mount pests
- permits
Sources for this job· O*NET 27-1012.00· ESCO/ISCO 3431
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