Somewhat Resilient
Last Update: 8/30/2026
AI Resilience Score for Math Science Occupations:
38.4%
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%).
Med
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
$81,490 median salary•200 annual openings•SOC Code: 15-2099.00
Mathematical Science Occupations, All Other are somewhat less resilient to AI impacts than most occupations, according to our analysis of 7 sources.
Mathematical science careers are labeled "Somewhat Resilient" because AI is genuinely changing the day-to-day work, automating routine tasks like data wrangling, coding, and report generation that used to take up a lot of a mathematician's time. The good news is that the deeper work, including scientific judgment, model validation, and building the math that makes AI itself function, is still very much a human job.
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Mathematical science careers are labeled "Somewhat Resilient" because AI is genuinely changing the day-to-day work, automating routine tasks like data wrangling, coding, and report generation that used to take up a lot of a mathematician's time. The good news is that the deeper work, including scientific judgment, model validation, and building the math that makes AI itself function, is still very much a human job.
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Math Science Occupations
Updated Quarterly
How is AI changing Math Science Occupations jobs?
The good news for anyone interested in a "mathematical sciences" career is that today's AI is mostly augmenting this work — not replacing it. The two big tasks you mentioned (processing data with computers and applying standard math formulas to real-world problems) are exactly where generative AI shines. According to Amstat News, "AI is speeding up routine statistical tasks such as coding and literature review, but the scientific judgment, domain expertise, and institutional knowledge central to statistical decision-making in pharmaceutical development remain difficult to automate, suggesting AI will reshape the profession rather" than replace it.
On the automation side, large-language-model "stat-bot" agents now handle data wrangling, exploratory analysis, and report generation [1], and DeepMind's AlphaProof reached silver-medal level on International Mathematical Olympiad problems [2]. But the SIAM AI Task Force Report of February 2026 [3] argues that applied mathematics is foundational infrastructure — developing the tools that allow AI systems to execute efficiently, obey physical laws, quantify uncertainty, generalize beyond training data, and resist adversarial manipulation. In other words, mathematicians are the ones building the AI that automates routine math.
Sources
How fast is AI adoption growing for Math Science Occupations?
Adoption is happening fast. The Stanford 2026 AI Index reports 88% organizational AI adoption [4], and generative AI is now used in at least one business function at 70% of firms. Because software is cheap compared to a mathematician's salary, employers eagerly deploy it — Research.com notes that employers now expect entry-level math grads to be proficient in automation techniques for modeling and statistics [5].
Yet demand for humans is still climbing: the U.S. Bureau of Labor Statistics projects data-scientist employment to grow 33.5% and operations-research analysts 21.5% from 2024–2034 [6]. Adoption is slowed a bit by ethical and legal caution — the Society of Actuaries emphasizes that human oversight, model validation, and professional judgment remain non-negotiable in regulated fields [7]. Translation: learn the AI tools, keep your reasoning skills sharp, and this career is looking bright.
Sources
Will AI replace Math Science Occupations?
Not entirely. We think AI will take over some tasks, but not the whole job.
Our 38.4% AI Resilience Score reflects a real tension here. AI tools are genuinely good at the routine parts of mathematical science work: data wrangling, exploratory analysis, and report generation are already being handled by automated systems [1]. Even DeepMind's AlphaProof reached silver-medal level on International Mathematical Olympiad problems [2]. So if your job is mostly running standard formulas or cleaning datasets, expect that part to shrink.
What stays human is the harder stuff: scientific judgment, domain expertise, and the ability to decide which questions are worth asking in the first place. Applied mathematicians are also the people building and validating the AI systems that automate routine math, which is not a small thing [3]. Regulated fields add another layer of protection, since professional oversight and model validation remain non-negotiable in many industries [7].
The economic picture gives some reason for optimism too. Wages and adaptive capacity both score well in our analysis, meaning this work tends to pay well and the skills transfer across industries. The practical advice: get comfortable with AI tools now, keep your reasoning sharp, and position yourself as someone who directs the math rather than just executes it.
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Latest AI news for Math Science Occupations
These articles highlight the promising landscape for students pursuing careers in "Mathematical Science Occupations, All Other." For instance, the Investopedia article emphasizes that strong math and computer science skills are crucial for high-paying AI jobs, essential for this field. Additionally, the analysis from Replaced by AI suggests that these occupations are at low risk of AI replacement, with a score of just 8/100. This indicates a resilient career path, where students can thrive by embracing AI tools while contributing their unique mathematical expertise.
Mathematical Science Occupations, All Other - AI Exposure
www.aiexposure.org • 9/20/2026
Mathematical Science Occupations, All Other face a risk score of 67/100 — 23 points above the national average of 44. With 82/100 GenAI exposure, ... Read more
Will AI Replace Mathematical Science Occupations, All Other?
www.replacedbai.com • 9/20/2026
Yes, Mathematical Science Occupations, All Other is relatively safe from AI replacement. With a risk score of 8/100, this occupation is in the low-risk ... Read more
Embracing artificial intelligence in the labour market
www.nature.com • 9/20/2026
by J Liu · 2024 · Cited by 68 — Our analysis, spanning from 2010 to 2022, reveals a significant trend: 11.16% of AI positions listings necessitate statistical expertise, ... Read more
Top College Degrees That Can Lead to the Highest-Paying AI Careers in 2026
www.investopedia.com • 8/20/2026
If you want to land a high-paying job in artificial intelligence, aim to grow your math and computer science skills—they matter more than...
Why AI won’t wipe out white-collar jobs
www.economist.com • 1/26/2026
Artificial intelligence reshapes white-collar jobs rather than eliminating them, with employment and wages rising since ChatGPT's launch...
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
$81,490
Jobs (2025)
4,000
Growth (2025-35)
+7.1%
Annual Openings
200
Education
Bachelor's degree
Experience
None
Source: Bureau of Labor Statistics, Employment Projections 2025-2035
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.
