Intelligence Brief Sciences Sector
Applied Mathematics
Bachelor's · 4 years
B-
Scorecard
- $100,000
- Median salary
- 23%
- Projected growth
- 53/100
- Difficulty
- 4
- Career paths
AI Resilience 62
Overall Score 64
CollegeRanker Degree Outlook Score™
61
out of 100 · B
Strong Outlook
Composite of earnings, projected growth, demand gap, AI resilience, career breadth, and remote flexibility — CollegeRanker's proprietary degree outlook model.
Supply vs Demand
High DemandMarket Demand80
Graduate Supply20
Demand outpaces graduate supply — projected 23% occupational growth (much faster than average).
Salary Trajectory
~5.8%/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
- Applied Mathematics scores 64/100 (B-), reflecting a balanced profile among bachelor's programs.
- Median salary of $100,000 reflects competitive earning potential.
- Projected growth of 23% significantly outpaces the national average.
- AI resilience score of 62 indicates moderate disruption risk across associated careers.
Applied Mathematics scores 64/100 — B-. The strongest dimension is growth (81/100), followed by salary (50/100). The biggest challenge: remote potential (35/100).
Research Insights
- Conditional Future-proof
Applied Mathematics is conditionally future-proof (66/100). The degree offers solid fundamentals but growth in some career pathways is slower than average. Strategic specialization can strengthen long-term positioning.
Score 66 /100 - Decent ROI
Applied Mathematics offers a moderate ROI (52/100). Salary outcomes are reasonable but the path to maximum earning requires additional credentials or specialization.
Score 52 /100 - Moderate Career Breadth
Applied Mathematics offers moderate career breadth (59/100). The 4 identified career paths provide options, but mobility across fields may require additional credentials or experience.
Score 59 /100
Decision Intelligence
Applied 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 struggle with abstract thinking or have a strong aversion to mathematics may find this degree challenging. Additionally, those expecting immediate job placement without gaining relevant experience or networking may be disappointed.
Student Archetypes
- The Analytical Thinker Recommended
This student thrives on solving complex problems using quantitative methods and enjoys exploring mathematical theories.
- The Career Switcher Conditional
This student is transitioning from a different field and seeks to leverage analytical skills in a new career path.
- The Passive Learner Not Recommended
This student prefers rote learning over problem-solving and lacks interest in applying mathematical concepts practically.
Economic Importance
Applied Mathematics plays a crucial role in various industries, including finance, technology, and healthcare. Its graduates are essential for solving complex problems through quantitative analysis, which is highly valued in today’s 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
Exceptional growth trajectory
Moderate barrier
Primarily in-person
Less competitive
Difficulty Score
53/100
Composite reflecting the combined demands of salary, growth, barrier, remote compatibility, and competition.
AI Resilience Assessment
Automation risk for careers linked to this degree.
Applied 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
Prospective students should be aware that while the job market is growing, competition can be fierce, particularly for top roles in prestigious companies. Moreover, practical experience through internships is often just as critical as academic credentials for career advancement.
-
Hiring Market Signal
Currently, the hiring market for applied mathematics graduates is robust, with strong demand from tech firms, financial institutions, and research organizations. Candidates with relevant internships or experience in data-related projects will have a competitive edge.
-
Risk Factors
- High student debt
- Job market saturation in specific regions
- Automation of analytical roles
- Rapid changes in technology
- Need for continuous skill development
-
ROI Timeline
Typically, graduates can expect to recoup their investment in this degree within 5 to 7 years, depending on starting salary and accumulated debt. Those entering high-paying industries may see quicker returns, while those in lower-paying positions may take longer.
What You'll Study
The curriculum's blend of theoretical and practical courses, such as Differential Equations and Mathematical Modeling, equips students with essential skills for tackling real-world challenges in data analysis and operational optimization.
In an Applied Mathematics program, students typically begin with foundational courses in calculus, linear algebra, and differential equations. As they progress, they delve into more specialized topics like statistical modeling, optimization, and numerical methods. The curriculum often includes hands-on projects and internships, where students apply their knowledge to real-world problems, enhancing their learning experience.
Many programs also incorporate collaborative projects that simulate real-life scenarios, requiring students to work in teams and develop solutions. These experiences can be challenging but provide essential skills that are highly valued in the job market.
Typical Curriculum
- Differential Equations
- Linear Algebra
- Numerical Analysis
- Optimization
- Mathematical Modeling
- Probability
- Computation
- Senior Project
Career Pipeline
From entry to executive.
Entry-Level
- Data Analyst
- Junior Quantitative Analyst
- Operations Research Assistant
- Mathematical Technician
- Research Assistant
Mid-Career
- Data Scientist
- Quantitative Analyst
- Operations Research Analyst
- Analytical Consultant
- Cryptographer
Advanced
- Chief Data Officer
- Senior Quantitative Analyst
- Director of Operations Research
Pipeline Insight
Graduates often begin in analytical roles, where they apply their technical skills, and those who excel often transition into more strategic roles by developing additional soft skills and industry knowledge.
Career Outcomes
Graduates of Applied Mathematics can expect a promising job market, with a projected growth rate of 23% for roles such as Data Scientists, Quantitative Analysts, and Operations Research Analysts. This demand is driven by the increasing reliance on data-driven decision-making in various sectors, leading to strong earnings potential and career advancement opportunities over time.
- Data Scientist
- Quantitative Analyst
- Operations Research Analyst
- Cryptographer
Compensation Context
The median salary of $100,000 reflects the high demand for applied mathematics professionals, driven by their ability to impact business decisions and operational efficiencies. Compensation can vary significantly based on geographic location, industry sector, and the complexity of the work involved.
Alternative Routes
Similar or competing pathways students consider alongside Applied Mathematics:
- Statistics
- Computer Science
- Operations Research
- Data Science bootcamps
- Mathematics degrees
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 building a solid foundation in mathematics during high school and consider internships to gain relevant experience.
Is This Degree Worth It?
This degree generally pays off well, especially in high-demand sectors where analytical skills are scarce. However, students should consider potential student debt and the competitive nature of job markets in certain geographic areas where opportunities may be limited.
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
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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.
