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Quantitative Risk Intern Jobs in Boston, MA (NOW HIRING)

DataRobot, Inc.

Agentic AI Intern

Boston, MA · On-site

DataRobot delivers AI that maximizes impact and minimizes business risk. Our platform and ... quantitative field * Working proficiency in Python (pandas, numpy) and comfort with data ...

DataRobot

Agentic AI Intern

Boston, MA · Remote

DataRobot delivers AI that maximizes impact and minimizes business risk. Our platform and ... quantitative field * Working proficiency in Python (pandas, numpy) and comfort with data ...

DataRobot

Agentic AI Intern

Boston, MA · Remote

DataRobot delivers AI that maximizes impact and minimizes business risk. Our platform and ... quantitative field * Working proficiency in Python (pandas, numpy) and comfort with data ...

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All JobsQuantitative Risk Intern JobsQuantitative Risk Intern Jobs in Boston, MA

Quantitative Risk Intern information

What are the key skills and qualifications needed to thrive as a Quantitative Risk Intern, and why are they important?

To thrive as a Quantitative Risk Intern, you need strong analytical skills, a solid understanding of statistics and probability, and progress toward a degree in finance, mathematics, or a related field. Familiarity with programming languages like Python or R, experience using statistical software, and knowledge of risk management frameworks are typically expected. Attention to detail, effective communication, and a proactive approach to problem-solving are valuable soft skills in this role. These competencies are crucial for accurately assessing financial risks and supporting data-driven decision-making in a fast-paced environment.

What types of projects or tasks can a Quantitative Risk Intern expect to work on during their internship?

As a Quantitative Risk Intern, you can expect to contribute to projects involving data analysis, financial modeling, and risk assessment for various portfolios or products. Typical tasks include gathering and cleaning large datasets, running statistical analyses, developing or refining risk models under supervision, and preparing reports to communicate findings to senior team members. Interns often collaborate closely with risk analysts, quantitative researchers, and sometimes IT teams, gaining exposure to both technical and business aspects of risk management. This hands-on experience provides valuable insight into industry-standard tools and methodologies, preparing you for a potential full-time role in quantitative finance.

What are Quantitative Risk Interns?

Quantitative Risk Interns are students or recent graduates who assist risk management teams in financial institutions by applying mathematical, statistical, and programming skills to analyze and manage financial risks. They typically work on projects that involve modeling risk exposures, stress testing portfolios, and supporting the development of risk management tools. This role provides hands-on experience with risk assessment processes, financial data analysis, and exposure to industry-standard software and methodologies. Interns also have the opportunity to learn from experienced risk professionals and gain insight into the decision-making processes that help institutions mitigate financial risks.
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Quantitative Risk Intern jobs near you
Infographic showing various Quantitative Risk Intern job openings in Boston, MA as of July 2026, with employment types broken down into 14% Internship, 1% As Needed, 53% Full Time, 29% Part Time, 2% Temporary, and 1% Contract. Highlights an 93% Physical, 3% Hybrid, and 4% Remote job distribution.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Posted 15 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)