Technology occupation
Will AI replace Data Scientists?
See how this occupation’s day-to-day work compares with other jobs in exposure to AI and automation, why it received this result, and where human skills remain essential.
Data Scientists
Higher AI & automation exposure than 98% of jobs we compare.
Exposure index: 98 / 100
This compares the type of work in this job with the work in hundreds of other jobs.
This is a comparison between jobs — not a 98% chance of job loss.
Why did this job get this result?
- Cleaning datasets, writing statistical code, and training machine learning models are digital, computer-intensive activities.
- Data pipelines, feature transformations, and metric calculations follow structured algorithmic and programming workflows.
- The occupation is entirely desk-based and digital, with no physical machinery components.
- Data scientists must formulate research questions, evaluate algorithm biases, and select appropriate mathematical modeling techniques.
- Communicating complex statistical findings, model limitations, and strategic business implications to stakeholders requires strong presentation skills.
How technology may change this work
Technology and human strengths
What AI & automation may affect
- Automated machine learning tools and code libraries can assist with model evaluation, data preprocessing, and routine statistical testing.
- Data preparation platforms can clean routine anomalies, handle missing values, and generate exploratory data visualizations.
Human strengths in this job
- Formulating relevant, high-impact business questions that quantitative data models can meaningfully address
- Detecting subtle data biases, confounding variables, and ethical risks in training datasets and algorithmic outputs
- Translating complex statistical probabilities into actionable business strategy for non-technical executives
- Evaluating whether model predictions make practical real-world sense beyond raw mathematical validation scores
How this result breaks down
These are model components, not percentages of the job or of how necessary people are.
How much the job involves information, data, writing, analysis or other digital work that AI and software may help with.
How much of the work follows regular, predictable or repeated steps.
How much of the physical work may be suitable for machines or robotics.
How much the job depends on judgment, creativity, communication and adapting to real situations.
How is the index calculated?
The Exposure Index is a percentile comparing this job's digital, routine, and physical tasks against all 897 occupations in the Model 2.1 dataset. A score of 98 means higher modeled exposure than approximately 98% of comparable jobs.
Your work may differ
How does your work compare?
Two people with the same job title can do very different work. Answer seven quick questions to see how your specific day-to-day tasks compare.
What people in this job commonly do
- Analyze, manipulate, or process large sets of data using statistical software.
- Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
- Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.
- Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.
- Recommend data-driven solutions to key stakeholders.
- Identify business problems or management objectives that can be addressed through data analysis.
How we compare jobs
We look at what people do in hundreds of occupations — including digital work, repeatable activities, physical automation and human skills — then compare the job with other occupations.
What this tool cannot predict
It cannot predict individual job loss, exact technology adoption timing or decisions by an individual employer.
Occupation and data details
O*NET-SOC: 15-2051.00
Career category: Technology
Typical education: Bachelor's degree
Data source: O*NET, U.S. Department of Labor.
This product includes information from the O*NET 31.0 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under the CC BY 4.0 license. Toolsyte has modified and scored some information; USDOL/ETA has not approved, endorsed, or tested these modifications. O*NET® is a trademark of USDOL/ETA.