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The AI Resilience Report helps you understand how AI is likely to impact your current or future career. Drawing on data from over 1,500 occupations, it provides a clear snapshot to support informed career decisions.
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Not Very Resilient
Last Update: 4/23/2026
Your role’s AI Resilience Score is
33.5%
Median Score
Meaningful human contribution
Measures the parts of the occupation that still require a human touch. This score averages data from up to four AI exposure datasets, focusing on the role’s resilience against automation.
Low
Long-term employer demand
Predicts the health of the job market for this role through 2034. Using Bureau of Labor Statistics data, it balances projected annual job openings (60%) with overall employment growth (40%).
Low
Sustained economic opportunity
Measures future earning potential and career flexibility. This score is a blend of total projected labor income (67%) and the role’s inherent ability to adapt to economic and technological shifts (33%).
High
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.
Most data sources align, with only minor variation. This is a well-supported result.
Contributing sources
AI Resilience Report forMathematical Science Occupations, All Other
Mathematical Science Occupations, All Other are less resilient to AI impacts than most occupations, according to our analysis of 6 sources.
This career is labeled as "Not Very Resilient" because many routine data tasks in mathematical work are increasingly automated by AI, such as data cleaning and running standard calculations. While humans still oversee and guide these processes, AI tools are handling more of the routine work, which can reduce the need for human labor in these areas.
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View analysisChat with CoachLatest newsMore career infoAnalysisChatNewsMoreThis role is not very resilient
This career is labeled as "Not Very Resilient" because many routine data tasks in mathematical work are increasingly automated by AI, such as data cleaning and running standard calculations. While humans still oversee and guide these processes, AI tools are handling more of the routine work, which can reduce the need for human labor in these areas.
Read full analysisAnalysis of Current AI Resilience
Math Science Occupations
Updated Quarterly • Last Update: 2/17/2026
How is AI changing Math Science Occupations jobs?
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
How fast is AI adoption growing for Math Science Occupations?
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
They solve complex problems by using math to analyze data, create models, and find patterns in various fields like science, business, or technology.
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
Apply standardized mathematical formulas, principles, and methodology to the solution of technological problems involving engineering or physical science.
2
Modify standard formulas so that they conform to project needs and data processing methods.
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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