Intelligence Brief Sciences Sector
Mathematics
Bachelor's · 4 years
C
Scorecard
- $104,280
- Median salary
- 8%
- Projected growth
- 47/100
- Difficulty
- 6
- Career paths
AI Resilience 62
Overall Score 53
CollegeRanker Degree Outlook Score™
54
out of 100 · B-
Solid Outlook
Composite of earnings, projected growth, demand gap, AI resilience, career breadth, and remote flexibility — CollegeRanker's proprietary degree outlook model.
Supply vs Demand
Healthy DemandMarket Demand62
Graduate Supply38
Demand modestly exceeds supply — projected 8% occupational growth (faster than average).
Salary Trajectory
~2%/yrModeled from BLS median wage and occupational growth. Dashed bars are forecast. Illustrative, not a guarantee.
Where Graduates Work
Common Employers
- Pfizer
- Merck
- Genentech
- National Labs
- Thermo Fisher
- Regeneron
Representative employers that commonly hire Sciences graduates — illustrative of where graduates concentrate, not a guarantee.
Industry Mix
- Pharma & Biotech 33%
- Research & Labs 24%
- Healthcare 16%
- Government 14%
- Other 13%
Estimated distribution of Sciences graduates across hiring industries.
Executive Summary
- Mathematics scores 53/100 (C), reflecting a challenging profile among bachelor's programs.
- Median salary of $104,280 reflects competitive earning potential.
- Projected growth of 8% is below the national average.
- AI resilience score of 62 indicates moderate disruption risk across associated careers.
Mathematics scores 53/100 — C. The strongest dimension is salary (52/100), followed by remote potential (35/100). The biggest challenge: growth (28/100).
Research Insights
- Conditional Future-proof
Mathematics is conditionally future-proof (54/100). The degree offers solid fundamentals but growth in some career pathways is slower than average. Strategic specialization can strengthen long-term positioning.
Score 54 /100 - Decent ROI
Mathematics offers a moderate ROI (56/100). Salary outcomes are reasonable but the path to maximum earning requires additional credentials or specialization.
Score 56 /100 - Moderate Career Breadth
Mathematics offers moderate career breadth (63/100). The 6 identified career paths provide options, but mobility across fields may require additional credentials or experience.
Score 63 /100
Decision Intelligence
Mathematics offers solid potential but requires strategic execution — the right concentration, school, and internships matter significantly to the outcome.
Who Benefits Most
Students who value career stability and meet the academic prerequisites. Students who pair this degree with internships and networking outperform peers. The moderate AI risk makes it important to specialize.
Who Should Think Twice
Individuals who dislike math or find it challenging should reconsider this degree, as it requires a strong aptitude for quantitative reasoning. Additionally, those seeking immediate job placement without further specialization might find the job market competitive.
Student Archetypes
- The Analytical Thinker Recommended
This student excels in logical reasoning and enjoys solving complex problems, often finding satisfaction in mathematical challenges.
- The Career Switcher Conditional
This individual seeks to transition into a data-driven field and is willing to invest time in learning advanced mathematical concepts.
- The Rote Memorizer Not Recommended
This student struggles with deep comprehension of mathematical concepts and relies on memorization rather than understanding.
Economic Importance
A Bachelor's degree in Mathematics plays a crucial role in various industries such as finance, technology, and academia, where analytical skills are essential for problem-solving and data analysis. The market values this degree for its rigorous training in quantitative reasoning, which is increasingly necessary in a data-driven economy.
Scorecard Analysis
Our proprietary scorecard evaluates degrees across five dimensions from BLS wage and growth data, O*NET work context, and standard education requirements.
Moderate earning potential
Below-average growth
Moderate barrier
Primarily in-person
Moderate competition
Difficulty Score
47/100
Composite reflecting the combined demands of salary, growth, barrier, remote compatibility, and competition.
AI Resilience Assessment
Automation risk for careers linked to this degree.
Mathematics faces moderate AI disruption risk (62/100). While AI will automate routine components within many associated careers, core responsibilities still require human oversight and strategic thinking. Upskilling in AI collaboration tools is recommended.
- Domain expertise from this degree provides some protection against full automation.
- AI can handle routine reporting, data aggregation, and first-pass analysis in many associated careers.
- Risk factor: entry-level roles in fields linked to this degree may face headcount reduction as AI handles more data processing.
Intelligence Deep Dive
-
Reality Check
The rigorous nature of this degree means that many students will face significant challenges in grasping complex concepts. Additionally, while the growth rate is favorable, competition for top roles can be intense, requiring continuous skill development.
-
Hiring Market Signal
Currently, the hiring market for mathematics graduates is robust, particularly in tech and finance sectors. Employers are actively seeking candidates with strong analytical skills, and job seekers should focus on building relevant experience through internships and projects.
-
Risk Factors
- High student debt
- Job market saturation in certain regions
- Rapid automation in data-related jobs
- High level of competition for prestigious roles
- Geographic concentration of job opportunities
-
ROI Timeline
Typically, it takes about 5-7 years to recoup the investment in a mathematics degree, depending on the starting salary and debt level. Graduates who enter high-paying fields like data science may recover their costs more quickly than those who choose lower-paying roles.
What You'll Study
The curriculum's focus on core areas such as calculus, linear algebra, and statistics equips students with a strong foundation in mathematical theory and practical applications. This combination prepares graduates for diverse roles that require critical thinking and quantitative analysis.
The academic experience in a Mathematics program typically involves a mix of core courses and electives, progressing from foundational topics to advanced theories. Students can expect to encounter challenging subjects like abstract algebra and real analysis, which require a solid grasp of earlier material. Group projects and individual research may feature prominently, with opportunities for internships that allow students to apply their knowledge in real-world scenarios.
Hands-on experiences can include computational labs where programming skills are developed, and internships may provide exposure to data analysis or financial modeling. Collaborative work is common, enhancing communication skills crucial for future careers.
Typical Curriculum
- Calculus Sequence
- Linear Algebra
- Abstract Algebra
- Real Analysis
- Probability & Statistics
- Differential Equations
- Numerical Methods
- Senior Seminar
Career Pipeline
From entry to executive.
Entry-Level
- Mathematics Tutor
- Data Analyst
- Actuarial Assistant
- Research Assistant
- Operations Research Analyst
Mid-Career
- Data Scientist
- Quantitative Analyst
- Actuary
- Operations Research Manager
- Cryptographer
Advanced
- Chief Data Officer
- Lead Quantitative Analyst
- Senior Operations Research Analyst
- Mathematics Professor
Pipeline Insight
Graduates typically advance by gaining experience and technical skills, often moving into specialized roles that require advanced mathematical techniques. Those who invest in continuous learning and networking are more likely to progress than those who remain stagnant.
Career Outcomes
Graduates with a Mathematics degree often find themselves in high-demand fields, with roles such as actuaries, data scientists, and quantitative analysts among the most common. The projected job growth of 8% across these professions indicates a robust market, driven by an increasing reliance on data and quantitative analysis in decision-making processes. As businesses and organizations continue to seek data-driven insights, the demand for skilled mathematicians is expected to grow.
- Actuary
- Data Scientist
- Quantitative Analyst
- Operations Research Analyst
- Cryptographer
- Mathematician
Compensation Context
The median salary of $104,280 reflects the high demand for mathematical skills across industries that rely on data analysis and complex problem-solving. Compensation can vary significantly based on geographic location, industry, and level of expertise, with urban centers often offering higher salaries due to cost of living and demand.
Alternative Routes
Similar or competing pathways students consider alongside Mathematics:
- Statistics
- Computer Science
- Physics
- Actuarial Science
- Self-taught Data Science
Getting In & Timeline
Typical time to complete: 4 years full-time
- High school diploma or equivalent
- Strong background in mathematics and science
- Standardized test scores (SAT/ACT) may be required
Advice
Prospective students should focus on developing a strong mathematical foundation in high school to succeed in this rigorous program.
Is This Degree Worth It?
This degree generally pays off well, especially for those who secure positions in high-demand fields like data science or actuarial work. However, it may not be worth the investment for students who struggle with quantitative subjects or do not pursue roles that leverage their mathematical skills.
Schools With Strong Outcomes in Sciences
Ranked by median graduate earnings 10 years after enrollment. Schools grouped into tiers by outcome level.
Top Tier2schools
Strong Outcomes2schools
Explore More Degrees
Methodology & Data Sources
Every score, grade, and verdict on this page is built from a consistent framework designed to answer one question: what is the expected return on this degree?
Scorecard dimensions. We evaluate programs on five proprietary axes — Salary, Job Growth, Education Barrier, Remote/Online Compatibility, and Competition — each normalized to a 0–100 scale. The Overall Score is a weighted composite: salary (30%), job growth (20%), AI resilience (15%), barrier proximity (15%), competition inverse (10%), and career breadth (10%). Letter grades follow a standard scale from A+ (95+) down to F.
AI Resilience. Measures automation risk across the degree's associated career pathways. Each degree receives a category-level baseline adjusted upward for AI-adjacent fields (e.g., machine learning, computer science) and downward for fields with higher routine-task exposure. The score represents the degree's resistance to labor-market disruption, not a prediction of elimination.
Verdict scores. Future-Proof, ROI, and Career Breadth are secondary composites weighting AI resilience, growth, salary, barrier, and career count to answer specific decision questions: is this career durable (Future-Proof), financially worthwhile (ROI), and flexible (Career Breadth)?
Data sources. Salary and growth figures are drawn from the Bureau of Labor Statistics Occupational Employment and Wage Statistics (O*NET) and the Occupational Outlook Handbook (2023–2033 projections). Education requirement data and work context scores come from O*NET 28.2. School-level earnings data is sourced from the Opportunity Insights Economic Tracker (median earnings 10 years after enrollment, based on federal tax records). Program rankings and school lists reflect CollegeRanker's proprietary classification and filtering methodology.
This page is built on disclosed, reproducible data. No affiliate bias, no survey-based rankings, no undisclosed weighting.
Data Behind This Page Updated 2025
Source datasets
- U.S. Bureau of Labor Statistics — Occupational Employment & Wage Statistics (OEWS)
- U.S. Bureau of Labor Statistics — Occupational Outlook Handbook, 2023–2033 projections
- O*NET 28.2 — education requirements and work-context data
- Opportunity Insights — earnings 10 years after enrollment (federal tax records)
Methodology
Degrees are scored on five normalized axes — salary (30%), job growth (20%), AI resilience (15%), education barrier (15%), and competition (10%), plus career breadth (10%) — each on a 0–100 scale.
See the full methodology and weights →Confidence notes
- Salary and growth figures come from federal Bureau of Labor Statistics data — administrative wage records and official projections, not surveys.
- AI-resilience scores are computed from O*NET task and work-context data, applied consistently across every program.
- Every measure is normalized to a fixed 0–100 scale, so degrees are directly comparable.
Limitations
- BLS wage data reflect national medians; actual pay varies widely by region, employer, and experience.
- Job growth is a 2023–2033 projection, not a guarantee — labor markets shift with technology and the economy.
- AI-resilience is a directional estimate of automation exposure, not a prediction about any specific role.
- Figures describe typical outcomes for the field, not a promise for any individual graduate.
