Automation index
How much of a Makeup artist’s job can AI already do?
A moodboard and a product list come earlier. Odd skin, a live show, and sweat do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Makeup artist
17%
AI can already do 17% of this job.
#601 / 643
View rankingMakeup artist
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 |
|---|---|---|---|
Moodboards, lists and quotes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 38% | +6.1 |
Bookings, consents and social.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 28% | +3.9 |
Make up, hair and retouch on set.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 6% | +1.4 |
Difficult skin, allergies and sweat.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 10% | +1.6 |
Talent, director and the clock.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Kit, hygiene and travel.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 17% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Moodboards, lists and quotes
- Bookings, consents and social
- Make up, hair and retouch on set
- Difficult skin, allergies and sweat
- Talent, director and the clock
- Kit, hygiene and travel
- maquillador
- makeup artist
- maquillaje
Sources for this job· O*NET 39-5091.00· ESCO/ISCO 5142
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