Mathematical Science Occupations, All Other
SOC Code: 15-2099.00
Computer & MathematicalMathematical Science Occupations in this residual category encompass all mathematical scientists not classified under more specific headings such as statisticians, actuaries, or operations research analysts. With a median salary of $71,490, these professionals apply mathematical theories, computational techniques, and quantitative reasoning to solve problems across science, engineering, business, and government. Their work spans a remarkable range of applications from cryptography to climate modeling.
Salary Overview
Median
$71,490
25th Percentile
$52,440
75th Percentile
$101,920
90th Percentile
$154,140
Salary Distribution
Job Outlook (2024–2034)
Growth Rate
+4.0%
New Openings
300
Outlook
As fast as average
Key Skills
Knowledge Areas
What They Do
- Analyze or manipulate bioinformatics data using software packages, statistical applications, or data mining techniques.
- Maintain awareness of new and emerging computational methods and technologies.
- Enter or retrieve information from structural databases, protein sequence motif databases, mutation databases, genomic databases or gene expression databases.
- Confer with researchers, clinicians, or information technology staff to determine data needs and programming requirements and to provide assistance with database-related research activities.
- Participate in the preparation of reports or scientific publications.
- Write computer programs or scripts to be used in querying databases.
- Create data management or error-checking procedures and user manuals.
- Extend existing software programs, web-based interactive tools, or database queries as sequence management and analysis needs evolve.
Tools & Technology
★ = Hot Technology (in-demand)
Education Requirements
Typical entry-level education: Bachelor's Degree
Related Careers
Top Career Pivot Targets
View all 16 →Careers with the highest skill compatibility from Mathematical Science Occupations, All Other.
A Day in the Life
A typical day for a mathematical science professional varies significantly depending on the specific role and industry. The morning might begin with reviewing data sets and running computational models to test hypotheses or optimize systems. Mid-morning could involve collaborating with scientists or engineers to translate real-world problems into mathematical frameworks that can be analyzed rigorously. Afternoon work often includes writing and debugging code in languages like Python, R, MATLAB, or Julia to implement algorithms and process results. Preparing visualizations and reports that communicate quantitative findings to non-technical stakeholders is a regular task. Some days are devoted to reading research literature to stay current with advances in mathematical methods applicable to ongoing projects. Team meetings provide opportunities to discuss approaches, troubleshoot analytical challenges, and coordinate on multi-phase projects. The work requires deep concentration, and many professionals structure their days to protect blocks of uninterrupted thinking time for complex problem-solving.
Work Environment
Mathematical science professionals typically work in office or academic settings that support concentrated analytical work. University environments provide research freedom, access to collaborators across departments, and opportunities to mentor students. Government agencies like the NSA, NASA, and Department of Energy employ mathematical scientists in secure facilities with classified research programs. Private sector workplaces range from financial institutions and technology companies to consulting firms and research laboratories. The work is primarily sedentary and computer-intensive, requiring extended periods of screen time for coding and analysis. Collaboration is increasingly important, with many projects requiring teamwork across disciplines including computer science, engineering, and domain-specific sciences. Remote work has become widely accepted in many mathematical science roles, as the work can often be performed independently with a computer and internet connection. Conference attendance and research presentations provide periodic breaks from routine and opportunities for professional networking.
Career Path & Advancement
Most positions in mathematical sciences require at minimum a bachelor's degree in mathematics, applied mathematics, or a quantitative field, though many roles prefer or require a master's or doctoral degree. Undergraduate programs build a foundation in calculus, linear algebra, differential equations, probability, and mathematical proof techniques. Graduate studies allow specialization in areas like numerical analysis, mathematical modeling, optimization, or discrete mathematics. Early career professionals often work as research assistants, analysts, or junior modelers, applying mathematical tools under the guidance of senior staff. With experience, professionals take on larger and more complex analytical projects, eventually leading research teams or analytical groups. Some advance into management positions overseeing quantitative analysis departments. Others pursue academic careers, combining research with teaching responsibilities at colleges and universities. Professional development through continuing education and certifications in specialized areas like data science or financial mathematics enhances advancement prospects.
Specializations
The breadth of mathematical science supports diverse specialization pathways beyond the standard named occupations. Cryptographers develop and analyze codes and encryption systems that protect sensitive data for governments and corporations. Mathematical modelers create simulations of complex systems like weather patterns, epidemiological spread, or financial markets. Topologists study properties of geometric objects that remain unchanged under continuous deformation, contributing to data analysis and robotics. Combinatorialists work on counting problems and discrete structures with applications in computer science, optimization, and network design. Computational mathematicians develop efficient numerical algorithms for solving large-scale scientific and engineering problems. Mathematical biologists apply quantitative methods to understand biological systems from gene regulation to population dynamics. Applied probability specialists work in telecommunications, insurance, and reliability engineering, analyzing systems subject to randomness.
Pros & Cons
Advantages
- ✓Intellectually stimulating work solving complex analytical challenges
- ✓Versatile skills applicable across numerous industries and sectors
- ✓Growing demand driven by data science, AI, and cybersecurity needs
- ✓Above-average median salary with strong earning potential in specialized roles
- ✓Remote work opportunities widely available in many positions
- ✓Collaborative work with experts from diverse scientific disciplines
- ✓Continuous learning through exposure to new problems and methods
Challenges
- ✗Advanced degree often required for research and senior positions
- ✗Work can be abstract and difficult to explain to non-technical audiences
- ✗Job titles and descriptions vary widely, making career navigation challenging
- ✗Highly specialized skills may limit opportunities in smaller job markets
- ✗Intense concentration required can lead to mental fatigue
- ✗Academic positions are highly competitive with limited openings
- ✗Keeping pace with rapidly evolving computational tools demands ongoing education
Industry Insight
The mathematical sciences are experiencing unprecedented demand as data-driven decision-making permeates every sector of the economy. Machine learning and artificial intelligence rely fundamentally on mathematical concepts from linear algebra, optimization, probability, and statistics. The cybersecurity sector needs mathematicians for cryptographic research as quantum computing threatens current encryption standards. Climate science and environmental modeling require sophisticated mathematical frameworks to predict and mitigate the effects of global change. Financial technology continues to demand quantitative professionals who can develop pricing models, risk assessments, and algorithmic trading strategies. The growth of computational biology and bioinformatics has created new opportunities for mathematicians in pharmaceutical and biotech companies. Government agencies are expanding their mathematical workforce for national security, defense, and intelligence applications. Despite strong demand, many employers report difficulty finding candidates with the right combination of mathematical depth and applied skills.
How to Break Into This Career
Entering mathematical science careers requires demonstrating strong quantitative aptitude and problem-solving abilities. Building a portfolio of projects during undergraduate or graduate studies that showcase applied mathematical skills impresses employers. Participating in mathematical competitions and research programs like SIAM undergraduate research or REU programs builds credentials and contacts. Developing strong programming skills alongside pure mathematics dramatically expands job opportunities in industry and government. Internships at government agencies, national laboratories, or technology companies provide exposure to applied mathematics in professional settings. Publishing research papers or presenting at conferences, even as a student, establishes credibility in the mathematical community. Networking through professional organizations like SIAM, AMS, or MAA connects aspiring professionals with mentors and job opportunities. Building expertise in a specific application domain, such as finance, biology, or cybersecurity, makes mathematical skills immediately actionable for employers.
Career Pivot Tips
Professionals with quantitative backgrounds from various fields can transition into mathematical science roles by building on their existing analytical foundations. Engineers apply their mathematical training and computational skills to modeling and simulation challenges in new domains. Physicists bring expertise in differential equations, statistical mechanics, and computational methods that translate broadly. Computer scientists contribute algorithm design and programming proficiency essential for modern mathematical work. Economists possess training in optimization, game theory, and statistical analysis directly applicable to mathematical science positions. Financial analysts understand quantitative modeling and risk assessment in real-world business contexts. Scientists from any discipline who have used mathematical modeling in their research can pivot by deepening their formal mathematical training. The most valuable transferable skills include logical reasoning, abstraction, programming proficiency, and the ability to formulate real-world problems in precise mathematical terms.