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.
U.S. Workers
2,220
Median Salary
$121,680
10-Year Growth
-0.7%
Annual Openings
100
Typical entry: Master's degree
11 of 11 tasks have some AI capability
Exposure Trend
This score reflects estimated AI technical capability for tasks in this occupation. It does not predict employment changes, and it does not account for company-specific constraints, regulation, or adoption barriers.
Apply mathematical theories and techniques to the solution of practical problems in business, engineering, the sciences, or other fields.
AI: Fully automatable - Given objectives and data, AI can apply mathematical theories and techniques to practical problems across domains with high reliability, automating much of the work.
Assemble sets of assumptions and explore the consequences of each set.
AI: Fully automatable - AI can systematically assemble assumption sets and explore their consequences through simulation and symbolic analysis, fully automating scenario exploration.
Address the relationships of quantities, magnitudes, and forms through the use of numbers and symbols.
AI: Fully automatable - Symbolic computation and numerical tools allow AI to handle relationships among quantities and forms automatically for most routine mathematical manipulation.
Perform computations and apply methods of numerical analysis to data.
AI: Fully automatable - Numerical analysis and data computation are highly automatable and can be performed end-to-end by AI and software.
Develop computational methods for solving problems that occur in areas of science and engineering or that come from applications in business or industry.
AI: Partial - AI can design and test computational methods and accelerate development, but novel algorithmic innovations and deep domain integration still require human research expertise.
Develop mathematical or statistical models of phenomena to be used for analysis or for computational simulation.
AI: Partial - Automated modeling tools and AutoML can construct statistical and mathematical models for many phenomena, but model formulation for novel or complex systems still needs human insight and validation.
Maintain knowledge in the field by reading professional journals, talking with other mathematicians, and attending professional conferences.
AI: Partial - AI can read and summarize journals and monitor conferences, but cannot fully replicate the informal networking and tacit knowledge exchange obtained by talking with peers and attending events.
Disseminate research by writing reports, publishing papers, or presenting at professional conferences.
AI: Partial - AI can draft reports, prepare manuscripts, and create presentations, but establishing original research claims, responding in live forums, and meeting authorship norms require human leadership.
Design, analyze, and decipher encryption systems designed to transmit military, political, financial, or law-enforcement-related information in code.
AI: Partial - AI can help design and analyze cryptosystems and perform many cryptanalytic tasks, but high-assurance, adversarial, and sensitive applications still require expert human oversight and classified context.
Develop new principles and new relationships between existing mathematical principles to advance mathematical science.
AI: Partial - AI can assist with conjecture generation, exploration, and sometimes proofs but cannot reliably originate deep, broadly accepted new mathematical principles on its own.
Conduct research to extend mathematical knowledge in traditional areas, such as algebra, geometry, probability, and logic.
AI: Partial - AI tools can extend knowledge in traditional mathematical areas by producing proofs, computations, and literature synthesis, but cannot yet autonomously drive consistently novel, high-impact research without human guidance.