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Virtual Applied Mathematics Intern Jobs in Boston, MA

Meta

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 ... Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual ...

FocusKPI Inc.

Data Science Intern

Boston, MA · On-site +1

Data Science Intern Statistical Modeling & Marketing Measurement Remote | Internship | Full-time | ... applied mathematics, econometrics, operations research, or a closely related quantitative field.

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Virtual Applied Mathematics Intern information

What is the difference between Virtual Applied Mathematics Intern vs Virtual Data Analyst?

AspectVirtual Applied Mathematics InternVirtual Data Analyst
Required CredentialsTypically pursuing or holding a degree in mathematics, applied mathematics, or related fieldOften requires a degree in statistics, data science, or related field
Work EnvironmentRemote internship, project-based tasks, research-focusedRemote or hybrid, data processing, analysis, and reporting tasks
Employer & Industry UsageResearch institutions, tech companies, finance, academiaBusiness, marketing, finance, tech industries

The Virtual Applied Mathematics Intern focuses on applying mathematical theories to research and problem-solving, often in academic or research settings. In contrast, the Virtual Data Analyst primarily interprets data to inform business decisions. Both roles require strong analytical skills and often similar educational backgrounds, but their work environments and end goals differ significantly.

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Infographic showing various Virtual Applied Mathematics Intern job openings in Boston, MA as of August 2026, with employment types broken down into 31% Internship, 61% Full Time, and 8% Part Time. Highlights an 85% In-person, and 15% Remote job distribution.

PhD Graduate Intern Quantitative Portfolio Risk Analytics

RiskAnalytics

Cambridge, MA • On-site

Other

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)

Apply