BETA
Updated: Mar 24
BETA
Updated: Mar 24
Changing fast
Last Update: 3/13/2026
Your role’s AI Resilience Score is
22.2%
Median Score
Changing Fast
Evolving
Stable
This reflects the reliability of your score based on the number of data sources available for this career and how closely those sources agree on the outlook. A higher confidence means more consistent evidence from labor experts and AI models.
What does this resilience result mean?
These roles are undergoing rapid transformation. Entry-level tasks may be automated, and career paths may look different in the near future.
AI Resilience Report for
Mathematical Science Occupations, All Other
They solve complex problems by using math to analyze data, create models, and find patterns in various fields like science, business, or technology.
This role is changing fast
The Mathematical Science Occupations are "Changing fast" because AI is automating many routine data tasks, like cleaning and merging data, which used to take up most of an analyst's time. While AI can handle these repetitive jobs quickly and efficiently, it still can't match human creativity and judgment needed for solving complex problems and developing new insights.
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The Mathematical Science Occupations are "Changing fast" because AI is automating many routine data tasks, like cleaning and merging data, which used to take up most of an analyst's time. While AI can handle these repetitive jobs quickly and efficiently, it still can't match human creativity and judgment needed for solving complex problems and developing new insights.
Read full analysisContributing Sources
We aggregate scores from multiple models and supplement with employment projections for a more accurate picture of this occupation’s resilience. Expand to view all sources.
AI Resilience
AI Resilience Model v1.0
AI Task Resilience
CareerVillage's proprietary model that estimates how resilient each occupation's tasks are to AI automation and augmentation
Changing fast16.0%
16.0%
Microsoft's Working with AI
AI Applicability
Measures how applicable AI tools (like Bing Copilot) are to each occupation based on real usage patterns
Changing fast6.3%
6.3%
Anthropic's Observed Exposure
AI Resilience
Based on observed patterns of how Claude is being used across occupational tasks in real conversations
Changing fast3.7%
3.7%
Althoff & Reichardt
Economic Growth
Measured as "Wage bill" which is a long term projection for average wage × employment. It's the total labor income flowing to an occupation
Evolving61.1%
61.1%
Low Demand
Labor Market Outlook
We use BLS employment projections to complement the AI-focused assessments from other sources.
Learn about this scoreGrowth Rate (2024-34):
4.0%
Growth Percentile:
62.9%
Annual Openings:
300
Annual Openings Pct:
2.2%
Analysis of Current AI Resilience
Math Science Occupations
Updated Quarterly • Last Update: 2/17/2026
What's changing and what's not
Many of the day‐to‐day data tasks in mathematical work are partly automated by software, but humans still control the process. For example, modern databases and analytics tools can automatically clean and merge data, fill in missing values, and run standard calculations. Researchers note that data processing often takes about 80% of an analyst’s time [1].
Software has automated routine steps like standardizing data formats, yet real projects usually involve messy or varied data. In these cases a person must decide how to fix problems or choose keys to join datasets [1] [1]. In short, “processing data for analysis” is heavily assisted by computers (hence the high 85% automation rating), but experts say humans still guide and check the work.
Likewise, when modifying standard formulas, AI tools can help but cannot fully replace a human mathematician. New “AI co-pilots” (such as code‐assistance models) can suggest how to write or adapt a formula to fit a data set or problem. For instance, mathematicians have used systems that propose the next step of a proof or calculation and then check it with software (the proof‐assistant Lean, for example) to ensure correctness [2] [3].
In practice, AI plays an augmenting role: it offers formula suggestions and even writes code, but the expert always reviews and edits those ideas [3] [2]. In other words, computers can do the routine “grunt work” of applying a known formula, but when a project needs a clever tweak or new insight, people do that part.
Sources
AI in the real world
There are good reasons businesses will happily use AI in mathematical jobs—but also reasons they will go slowly. On the plus side, AI tools for data and math work are widely available (in cloud services, Python libraries, etc.), and they promise big savings. In fact, one finance study reported that if companies fully use AI, they could cut U.S. business costs by about \$920 billion per year, mostly by reducing labor needs [4].
A recent survey also found that by 2024 about 78% of companies were already using some form of AI to work smarter [3]. These economic incentives (cheaper 24/7 computing, faster results) push firms to adopt AI where they can. Moreover, there is a shortage of skilled math analysts in many fields, so tools that handle routine parts of the job let the limited experts focus on creative work.
On the other hand, adoption will be cautious. Mathematical jobs often involve creative thinking and judgment, not just rote tasks. Experts point out that today’s AI is “narrow”: it handles well-defined, repetitive tasks but struggles with open-ended reasoning [5].
In practice, systems must be trained and checked carefully. Advanced AI models cost a lot to build and require skilled staff to run, so smaller teams may delay using them. There is also awareness of risks: news stories and research note that AI can make confident mistakes (“hallucinations”), so mathematicians insist on verifying any AI‐produced formula [2] [3].
Finally, no laws forbid math-using AI, but the field values correctness above all. For example, the proof-assistant Lean will reject any AI suggestion that isn’t provably correct [2].
In healthy fields like mathematical sciences, people expect AI to play the role of a powerful assistant. It won’t replace human creativity and judgment. Many experts stress that future “augmented mathematicians” will use AI tools to do tedious data work and check computations [2] [3], while the humans tackle the hard concepts and big-picture insight.
In other words, AI can automate the routine steps (making jobs faster or changing them), but the unique skills of mathematicians – problem‐solving, abstract thinking, and setting research directions – remain very valuable.
Sources
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More Career Info
Career: Mathematical Science Occupations, All Other
Parent Careers
Employment & Wage Data
Median Wage
$71,490
Jobs (2024)
5,000
Growth (2024-34)
+4.0%
Annual Openings
300
Education
Bachelor's degree
Experience
None
Source: Bureau of Labor Statistics, Employment Projections 2024-2034
Task-Level AI Resilience Scores
AI-generated estimates of task resilience over the next 3 years
1
Modify standard formulas so that they conform to project needs and data processing methods.
2
Apply standardized mathematical formulas, principles, and methodology to the solution of technological problems involving engineering or physical science.
3
Reduce raw data to meaningful terms, using the most practical and accurate combination and sequence of computational methods.
4
Translate data into numbers, equations, flow charts, graphs, or other forms.
5
Process data for analysis, using computers.
Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.
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