Automation index
How much of a Geneticist’s job can AI already do?
A panel comes earlier. Reading the variant and talking to the family do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Geneticist
27%
AI can already do 27% of this job.
#154 / 623
View rankingGeneticist
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 |
|---|---|---|---|
Panel, variants and the report.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Clinic, the pedigree and the family.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
VUS, the doubt and not scaring extra.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Lab, the sample and the one that is not enough.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The clinician, the treatment and not jumping to a yes.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Read it, and talking to the family.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- genetic panels
- variants
- pedigree
- VUS
- genetic reports
- samples
- genetics clinic
- not scare
Sources for this job· O*NET 19-1029.00· ESCO/ISCO 2131
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