Automation index
How much of a Biologist’s job can AI already do?
Studies organisms and ecosystems, in the field, the lab or consulting.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Biologist
31%
AI can already do 31% of this job.
#30 / 623
View rankingBiologist
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 |
|---|---|---|---|
Teach or supervise students and perform research at universities and colleges.EstimateNo public source provides a precise figure. This is our estimate. | 17% | 22% | +3.7 |
Identify, classify, and study structure, behavior, ecology, physiology, nutrition, culture, and distribution of plant and animal species.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Program and use computers to store, process, and analyze data.EstimateNo public source provides a precise figure. This is our estimate. | 15% | 48% | +7.2 |
Supervise biological technicians and technologists and other scientists.EstimateNo public source provides a precise figure. This is our estimate. | 15% | 22% | +3.3 |
Research environmental effects of present and potential uses of land and water areas, determining methods of improving environmental conditions or such outputs as crop yields.EstimateNo public source provides a precise figure. This is our estimate. | 6% | 22% | +1.3 |
Collect and analyze biological data about relationships among and between organisms and their environment.EstimateNo public source provides a precise figure. This is our estimate. | 6% | 48% | +2.9 |
Study basic principles of plant and animal life, such as origin, relationship, development, anatomy, and function.EstimateNo public source provides a precise figure. This is our estimate. | 5% | 28% | +1.4 |
Review reports and proposals, such as those relating to land use classifications and recreational development, for accuracy, adequacy, or adherence to policies, regulations, or scientific standards.EstimateNo public source provides a precise figure. This is our estimate. | 5% | 55% | +2.8 |
Other less frequent tasks (weight estimated from frequency).EstimateNo public source provides a precise figure. This is our estimate. | 15% | 28% | +4.2 |
| Total | 100% | 31% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- microbiology-bacteriology
- genetics
- biotechnology
- laboratory techniques
- biology
- medical genetics
- genetic engineering
- scientific research methodology
- manage research data
- perform laboratory tests
Sources for this job· O*NET 19-1029.04· 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