Data Scientists: daily tasks & AI impact
This page breaks Data Scientists into the top 15 daily O*NET tasks and scores each for Generative AI automation probability. Those task scores roll up to an overall vulnerability index of 66/100 (Moderate automation risk). Struck-through rows are ≥80% automatable; highlighted safer rows are under 30% or primarily physical.
High-automation exposure
No task in this set currently exceeds the 80% threshold, so risk is spread across moderate probabilities rather than a few catastrophic duties.
Durable human work
This profile has little “safe” task mass under our thresholds. Workers should treat AI fluency as mandatory and actively map transferable skills into lower-risk roles.
Importance comes from O*NET incumbent ratings. Automation probability is model-generated for current Generative AI capability (not speculative AGI). Digital / physical / mixed labels describe the modality of the work, which strongly correlates with near-term automation potential.
| Task | Importance | AI probability | Nature |
|---|---|---|---|
| Analyze, manipulate, or process large sets of data using statistical software. n/a | — | — | — |
| Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use. n/a | — | — | — |
| Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods. n/a | — | — | — |
| Clean and manipulate raw data using statistical software. n/a | — | — | — |
| Compare models using statistical performance metrics, such as loss functions or proportion of explained variance. n/a | — | — | — |
| Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software. n/a | — | — | — |
| Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users. n/a | — | — | — |
| Design surveys, opinion polls, or other instruments to collect data. n/a | — | — | — |
| Identify business problems or management objectives that can be addressed through data analysis. n/a | — | — | — |
| Identify relationships and trends or any factors that could affect the results of research. n/a | — | — | — |
| Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis. n/a | — | — | — |
| Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques. n/a | — | — | — |
| Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies. n/a | — | — | — |
| Recommend data-driven solutions to key stakeholders. n/a | — | — | — |
| Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest. n/a | — | — | — |
What remains human
No tasks currently fall under the safe threshold for this occupation.
FAQ
Which Data Scientists tasks are most exposed to AI?
No task in this set currently exceeds the 80% threshold, so risk is spread across moderate probabilities rather than a few catastrophic duties.
Which Data Scientists tasks are safest from Generative AI?
This profile has little “safe” task mass under our thresholds. Workers should treat AI fluency as mandatory and actively map transferable skills into lower-risk roles.
How should I read the Data Scientists task table?
Importance comes from O*NET incumbent ratings. Automation probability is model-generated for current Generative AI capability (not speculative AGI). Digital / physical / mixed labels describe the modality of the work, which strongly correlates with near-term automation potential.