Data Scientists
Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets. Visualize, interpret, and report data findings. May create dynamic data reports.
BLS SOC 15-2051 · O*NET-SOC 15-2051.00 · BLS 2025–2035 · O*NET 31.0
Median annual wage, 2025
$120,230
Projected change, 2025–2035
34.6%
2025–2035 annual average openings
24,800
AI exposure
Very high
Editorial analysis
AI can build models. Data scientists still have to decide what is worth modeling.
Data scientists are both users of AI and builders of the systems behind it. New tools can write analysis code, suggest visualizations, clean data, generate features, compare models, explain statistical methods, and create natural-language summaries of results. That reduces much of the mechanical work between receiving a dataset and exploring it.
It also exposes an old truth more clearly: sophisticated modeling cannot rescue a badly framed question.
The hard parts were never only mathematical
- Determine whether the available data actually represents the phenomenon being studied.
- Choose a useful business or scientific question rather than merely an interesting model.
- Separate genuine predictive signal from leakage, bias, coincidence, and artifacts.
- Explain uncertainty well enough that decision-makers understand what the result can and cannot support.
Some routine modeling work may become accessible to analysts who are not data scientists, but the demand for deeper expertise can remain strong where the consequences are significant or the data is messy. Data scientists who combine statistics, experimentation, engineering, domain knowledge, and AI evaluation may have an advantage over those whose expertise is mainly operating a particular modeling tool.
Labor-market picture
Two measures, different questions
These measures offer two different ways to understand the occupation: BLS projections show how national employment may change over time, while relative AI exposure highlights where tasks may intersect with AI-related capabilities. Read them with the work profile and your local context to decide what deserves closer investigation.
Employment
- Employment, 2025
- 275,600 jobs
- Projected employment, 2035
- 371,000 jobs
- Projected change, 2025–2035
- 95,400 jobs (34.6%)
Relative AI exposure
BLS category: Very high. This is a relative task-exposure classification, not a forecast of employment change, automation, wages, or worker replacement.
Relative AI exposure · Very high
Work profile
Representative tasks
These task statements come from O*NET 31.0. They are source facts, not labels assigned by the AI exposure measure.
- 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.
Skills that matter
Skills in the source profile
Importance uses O*NET's 1–5 scale. Level uses its 0–7 scale. Select a skill to see its source definition and reported values.
Technology and tools
These technology records come from O*NET and show software or tools reported in the occupation profile. They describe work context, not a required checklist for every employer.
- Business intelligence and data analysis software
- Data base user interface and query software
- Storage networking software
- Cloud-based management software
- Procedure management software
- Data base management system software
- Development environment software
- Industrial control software
Preparation
- Typical entry education
- Bachelor's degree
- Related experience
- None
- On-the-job training
- None
Ask JobShift
Ask Dr. Morgan Reed about this evidence
Morgan is the JobShift Atlas analyst powered by AI. Answers draw from the evidence on this page and explain what it can tell you.
Prepare for a direction
Turn occupation research into a next action
Next action
Explore a related data analyst resume example
Opens a closely related role resource on resumekicker.com. Your JobShift search and selections are not shared with external sites.
Next action
Practice data scientist interview questions
Opens a role-specific resource on interviewkicker.com. Your JobShift search and selections are not shared with external sites.
Next action
Explore independent work as one option
Opens a partner resource for your next step on www.ideatomarketai.com. Your JobShift search and selections are not shared with external sites.
Source provenance
Labor-market and exposure facts: bls-ep-2025-35. Work profile: onet-31.0. Verify release details on the Sources page.