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Applied Mathematics Intern Jobs in Needham, MA (NOW HIRING)

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Research Scientist Intern, AI Alignment

Boston, MA

$7.6K - $12K/mo

... applied mathematics and security/privacy. Our interns have an opportunity to make core algorithmic ... Research Scientist Intern, AI Alignment Responsibilities: * Develop novel state-of-the-art ...

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Applied Mathematics Intern information

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How much do applied mathematics intern jobs pay per hour?

As of Aug 29, 2026, the average hourly pay for applied mathematics intern in Needham, MA is $21.07, according to ZipRecruiter salary data. Most workers in this role earn between $16.78 and $23.56 per hour, depending on experience, location, and employer.

What does an applied mathematics intern do?

An Applied Mathematics Intern applies mathematical concepts and computational techniques to solve real-world problems in various industries, such as finance, engineering, or data science. They assist with data analysis, model development, algorithm implementation, and simulations. Interns may work with teams to apply statistical methods, optimization, or machine learning to practical challenges. This role provides hands-on experience in applying theoretical knowledge to industry-specific problems while developing programming and analytical skills.

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

To thrive as an Applied Mathematics Intern, you generally need a solid foundation in mathematical analysis, statistical methods, and problem-solving skills, often supported by progress toward a degree in mathematics, statistics, or a related field. Experience with programming languages such as Python, MATLAB, or R, and familiarity with data analysis or mathematical modeling software, are highly valuable. Excellent communication, adaptability, and teamwork are soft skills that help interns effectively share findings and collaborate with diverse teams. These skills ensure interns can translate complex data into actionable solutions, contribute meaningfully to projects, and adapt to varied real-world applications.

Is applied mathematics in demand?

Applied mathematics is in high demand across industries such as finance, data analysis, engineering, and technology, where skills in modeling, statistical analysis, and programming are valued. Many organizations seek professionals with strong mathematical and problem-solving abilities, making roles like applied mathematics intern valuable for gaining practical experience in these fields.

What internships can I get with an applied mathematics intern?

Applied mathematics interns can pursue internships in data analysis, finance, engineering, research, and technology companies. These roles often involve skills in programming, statistical modeling, and problem-solving, and may be available in industries such as finance, software development, or scientific research.

What jobs can I do with applied mathematics?

Applied mathematics graduates can pursue roles such as data analyst, quantitative analyst, operations researcher, financial analyst, or software developer. These jobs often require strong analytical, problem-solving, and programming skills, and may involve working with statistical software, mathematical modeling, or data visualization tools.

Which internship is best for applied mathematics students?

The best internship for applied mathematics students typically involves roles in data analysis, quantitative research, or modeling, often offered by finance, technology, or research organizations. These internships usually require strong analytical skills, proficiency in programming languages like Python or MATLAB, and a solid understanding of mathematical concepts. They provide practical experience in applying mathematical theories to real-world problems and may lead to full-time opportunities in related fields.

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Applied Mathematics Intern jobs near you

PhD Graduate Intern Quantitative Portfolio Risk Analytics

Cambridge, MA • On-site

RiskAnalytics
See more jobs at RiskAnalyticsexternal

Other

Posted 26 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)

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