Automation index
How much of a Mathematician’s job can AI already do?
A symbolic calc and a bibliography come earlier. Seeing the pattern, and writing a proof that convinces, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Mathematician
28%
AI can already do 28% of this job.
#108 / 623
View rankingMathematician
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 |
|---|---|---|---|
Symbolic calc, examples and paper search.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 52% | +11.4 |
Draft proofs and exposés.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 40% | +7.2 |
Find the argument and the counter-example.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 15% | +2.7 |
Lecture, teach and argue in seminar.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Peer-review and polish the writing.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
Choose the problem that is worth years.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 16% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Symbolic calc, examples and paper search
- Draft proofs and exposés
- Find the argument and the counter-example
- Lecture, teach and argue in seminar
- Peer-review and polish the writing
- Choose the problem that is worth years
- matematic
- mathematician
Sources for this job· O*NET 15-2021.00· ESCO/ISCO 2120
IS WORK OVER?
ML engineer
29%
#64 / 623
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