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Applied Mathematics Internship Jobs (NOW HIRING)

Eight Sleep

AI/ML Research Internship

San Francisco, CA · On-site

Excellent mathematical skills in linear algebra and statistics * Ability to collaborate with others * Problem solving skills * Applied ML Engineering internships: Experience with integrating research ...

Eight Sleep

AI/ML Research Internship

San Francisco, CA · On-site

Excellent mathematical skills in linear algebra and statistics * Ability to collaborate with others * Problem solving skills * Applied ML Engineering internships: Experience with integrating research ...

Intego Group

UCF Clinical SAS Intern

Florida, NY · On-site

... in Applied Mathematics, Statistics or Data Science, but the program is open for anyone with a strong mathematical background. Internship Program Format: Internships are part‐time, paid positions ...

ConnectPrep

Data Analyst Internship

Washington, DC · Remote

You are studying (or have studied) Applied Mathematics, Statistics, Computer Science, Data Science ... Prior internship or project experience in data analysis, business intelligence, or marketing ...

ConnectPrep

Data Analyst Internship

Washington, DC · On-site +1

You are studying (or have studied) Applied Mathematics, Statistics, Computer Science, Data Science ... Prior internship or project experience in data analysis, business intelligence, or marketing ...

Intego Group

UCF Clinical SAS Intern

Florida, NY · On-site

... in Applied Mathematics, Statistics or Data Science, but the program is open for anyone with a strong mathematical background. Internship Program Format: Internships are part‐time, paid positions ...

ConnectPrep

Data Analyst Internship

Washington, DC · On-site +1

You are studying (or have studied) Applied Mathematics, Statistics, Computer Science, Data Science ... Prior internship or project experience in data analysis, business intelligence, or marketing ...

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Applied Mathematics Internship information

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$34

How much do applied mathematics internship jobs pay per hour?

As of Sep 3, 2026, the average hourly pay for applied mathematics internship in the United States is $19.34, according to ZipRecruiter salary data. Most workers in this role earn between $15.38 and $21.63 per hour, depending on experience, location, and employer.

What is an applied mathematics internship?

An applied mathematics internship is a temporary position where students or recent graduates work with organizations to apply mathematical theories and techniques to solve real-world problems. Interns typically assist in data analysis, modeling, simulations, or algorithm development in fields like finance, engineering, technology, or research. These internships provide valuable hands-on experience, help build professional networks, and often enhance job prospects after graduation.

What are applied mathematics internship opportunities?

Applied mathematics is the use of mathematical formulas and methods to solve real-world problems and develop innovations. It is used in many different industries, including computer science, biological science, engineering, aeronautics, and business. Applied mathematics internships are typically open to students majoring in or who have graduated with a degree in applied mathematics and plan to pursue a career utilizing their training. Research institutes often offer these internships, but you can also find them at companies that use significant amounts of math in their day-to-day activities. Your specific job duties vary, depending on the company and industry, but you are usually paired up with a professional in the field to learn more about how applied mathematics is utilized. Most of these internships occur over the summer, though some companies offer internships year-round.

What are the key skills and qualifications needed to thrive as an applied mathematics intern, and why are they important?

To thrive as an Applied Mathematics Intern, you need a solid background in mathematics, statistics, and problem-solving skills, often supported by current enrollment in or completion of a relevant degree program. Familiarity with tools such as MATLAB, Python, R, and statistical software is typically required, along with experience using data analysis and modeling systems. Strong analytical thinking, attention to detail, and effective communication are vital soft skills for collaborating with teams and presenting complex findings. These skills and qualifications are crucial for solving real-world problems, contributing to research projects, and translating mathematical concepts into actionable solutions.

What types of projects can I expect to work on during an applied mathematics internship?

During an Applied Mathematics Internship, you’ll typically work on projects involving data analysis, mathematical modeling, or algorithm development to solve real-world problems. Interns often collaborate closely with teams in engineering, data science, or research, contributing to tasks such as optimizing business processes, analyzing large datasets, or creating simulations. The work environment is usually highly collaborative and may require presenting your findings to both technical and non-technical stakeholders. These projects help interns develop practical skills and gain exposure to diverse applications of mathematics in industry.
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Applied Mathematics Internship jobs near you
Infographic showing various Applied Mathematics Internship job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 25% Part Time, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $40,232 per year, or $19.3 per hour.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Re-posted 29 days ago

Job description

Ph.D. Graduate Intern – Quantitative Portfolio Risk Analytics (Cross-Disciplinary)

Position Overview
We are seeking an exceptional Ph.D. graduate student to join our team as a Quantitative Portfolio Risk Analytics Intern. This role focuses on developing and applying advanced analytical methods to understand portfolio risk, market structure, and complex financial systems.
We are intentionally recruiting from cross-disciplinary, research-driven backgrounds. Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics, statistics, engineering, economics, computer science, quantum computing, biotech, and other data-intensive sciences are strongly encouraged to apply—especially those interested in translating rigorous quantitative methods into real-world financial applications.
Key Responsibilities
  • Develop and enhance quantitative models for portfolio risk, including factor-based and statistical approaches 
  • Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of risk 
  • Design and implement analytical tools and pipelines using Python and SQL 
  • Contribute to model validation, backtesting, and performance evaluation 
  • Collaborate with risk, engineering, and data teams to improve model scalability and data infrastructure 
  • Communicate complex quantitative insights through clear visualizations and technical summaries 
  • Apply advanced methodologies from your discipline (e.g., stochastic modeling, optimization, machine learning, or geometric/topological approaches) to improve risk analytics 
Required Qualifications
  • Currently enrolled in a graduate Ph.D. program in a highly quantitative field (e.g., Math, Applied Mathematics, Physics, Astrophysics, Statistics, Computer Science, Engineering, Financial Engineering, Economics, Biotech or other data-driven disciplines) 
  • Strong foundation in probability, statistics, and numerical methods 
  • Proficiency in Python (NumPy, pandas, or similar) and/or SQL 
  • Experience working with large datasets and implementing quantitative models 
  • Ability to think rigorously about complex systems and translate theory into practical solutions 
Preferred Qualifications
  • Familiarity with quantitative finance concepts (e.g., portfolio theory, factor models, volatility modeling, Value-at-Risk) 
  • Experience with scientific computing, optimization, or machine learning 
  • Background or research in cross-disciplinary areas such as: 
    • Statistical physics, complex systems, or network theory 
    • Applied or computational mathematics 
    • Machine learning or probabilistic modeling 
    • Quantum computing or advanced optimization techniques 
    • Topological data analysis or geometric data methods 
  • Prior research, publications, or project work demonstrating advanced quantitative modeling 
What You’ll Gain
  • Exposure to real-world portfolio risk problems at the intersection of finance and advanced analytics 
  • Opportunity to apply cutting-edge academic methods in a production environment 
  • Collaboration with a highly quantitative, cross-disciplinary team 
  • Experience working with large-scale financial data and modern analytics infrastructure 
  • Mentorship and potential pathway to full-time quantitative roles 
Duration & Compensation
  • Internship: Summer 2026, with potential to extend 
  • Paid internship (competitive, based on experience and location)