Automation index
How much of a Sociologist’s job can AI already do?
A survey and a table come earlier. Understanding what is actually happening does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Sociologist
25%
AI can already do 25% of this job.
#252 / 623
View rankingSociologist
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 |
|---|---|---|---|
Surveys, tables and a first report.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Design the research and the questions.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Field, interviews and groups.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Public client, ethics and outreach.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Literature, theory and review.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Not selling a chart that does not say that.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Surveys, tables and a first report
- Design the research and the questions
- Field, interviews and groups
- Public client, ethics and outreach
- Literature, theory and review
- Not selling a chart that does not say that
- sociologo
- sociologist
Sources for this job· O*NET 19-3041.00· ESCO/ISCO 2632
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