Automation index
How much of a Chemist’s job can AI already do?
A protocol and a batch report come earlier. The smell of a reaction, and signing a certificate, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Chemist
26%
AI can already do 26% of this job.
#209 / 623
View rankingChemist
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 |
|---|---|---|---|
Protocols, calcs and safety sheets.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 45% | +9 |
Batch reports, charts and docs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 40% | +7.2 |
Synthesis, titration and sample handling.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 15% | +2.7 |
Read a strange result and repeat it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Plant safety and waste.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
Sign quality and talk to production.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Protocols, calcs and safety sheets
- Batch reports, charts and docs
- Synthesis, titration and sample handling
- Read a strange result and repeat it
- Plant safety and waste
- Sign quality and talk to production
- quimico
- chemist
- laboratorio quimico
Sources for this job· O*NET 19-2031.00· ESCO/ISCO 2113
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