# Mathematicians

> Conduct research in fundamental mathematics or in application of mathematical techniques to science, management, and other fields. Solve problems in various fields using mathematical methods.

- **SOC code:** 15-2021.00
- **Canonical URL:** https://singulariki.com/roles/role-15-2021-00
- **Also known as:** Computational Scientist, Cryptographer, Mathematician, Research Scientist, Agent-Based Modeler, Computational Mathematician, Cryptographic Vulnerability Analyst, Research Computing Specialist
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Mentor others on mathematical techniques.
- Maintain knowledge in the field by reading professional journals, talking with other mathematicians, and attending professional conferences.
- Develop new principles and new relationships between existing mathematical principles to advance mathematical science.
- Disseminate research by writing reports, publishing papers, or presenting at professional conferences.
- Assemble sets of assumptions, and explore the consequences of each set.
- Perform computations and apply methods of numerical analysis to data.
- Address the relationships of quantities, magnitudes, and forms through the use of numbers and symbols.
- Conduct research to extend mathematical knowledge in traditional areas, such as algebra, geometry, probability, and logic.
- Apply mathematical theories and techniques to the solution of practical problems in business, engineering, the sciences, or other fields.
- Develop mathematical or statistical models of phenomena to be used for analysis or for computational simulation.
- Develop computational methods for solving problems that occur in areas of science and engineering or that come from applications in business or industry.
- Design, analyze, and decipher encryption systems designed to transmit military, political, financial, or law-enforcement-related information in code.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Mathematics _(essential_skill)_
- Mathematical Reasoning _(ability)_
- Mathematics _(knowledge)_
- Number Facility _(ability)_
- Critical Thinking _(essential_skill)_
- Oral Comprehension _(ability)_
- Written Comprehension _(ability)_
- Oral Expression _(ability)_
- Written Expression _(ability)_
- Reading Comprehension _(essential_skill)_
- Active Learning _(essential_skill)_
- Complex Problem Solving _(transferable_skill)_

**Skills in demand:**
- Mathematics _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Complex Problem Solving _(Common Skill)_
- Active Learning _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Inductive Reasoning _(Common Skill)_
- Writing _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Active Listening _(Common Skill)_
- English Language _(Common Skill)_

**Tools & technology:**
- Atlassian JIRA _(hot technology, in demand)_
- C _(hot technology, in demand)_
- C# _(hot technology, in demand)_
- C++ _(hot technology, in demand)_
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Python _(hot technology, in demand)_
- R _(hot technology, in demand)_
- The MathWorks MATLAB _(hot technology, in demand)_
- Adobe Photoshop _(hot technology)_
- Apple macOS _(hot technology)_
- Bash _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 100th percentile (High) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 99th percentile (High) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 95th percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 99th percentile (High) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 23rd percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** yes — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -0.7% growth (Declining); 0.1k annual openings; 2.4k → 2.4k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $121,680; 2,220 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** 53% automation, 45% augmentation (usage-weighted).
- **Autonomy median:** 4.0 (higher = AI acts more independently).
- **Dominant collaboration mode:** directive.

**Tasks most handed to AI here:**
- Address the relationships of quantities, magnitudes, and forms through the use of numbers and symbols. _(14.3% of measured AI use; directive)_
- Conduct research to extend mathematical knowledge in traditional areas, such as algebra, geometry, probability, and logic. _(3.2% of measured AI use; directive)_
- Develop mathematical or statistical models of phenomena to be used for analysis or for computational simulation. _(2.6% of measured AI use; directive)_
- Develop new principles and new relationships between existing mathematical principles to advance mathematical science. _(1.6% of measured AI use; learning)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me address the relationships of quantities, magnitudes, and forms through the use of numbers and symbols.
- Help me conduct research to extend mathematical knowledge in traditional areas, such as algebra, geometry, probability, and logic.
- Help me develop mathematical or statistical models of phenomena to be used for analysis or for computational simulation.
- Help me develop new principles and new relationships between existing mathematical principles to advance mathematical science.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-15-2021-00_