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Quantitative Intern Jobs in Randolph, MA (NOW HIRING)

PanAgora

Intern, Dynamic

Boston, MA · On-site

$2.5K - $4.5K/wk

We seek to provide investment solutions using sophisticated quantitative techniques that ... As an intern, you will conduct special projects for the team in the management of active U.S ...

DataRobot

Agentic AI Intern

Boston, MA · Remote

Each AI Native Intern is embedded in a real team, working on real problems, and is expected to ... quantitative field * Working proficiency in Python (pandas, numpy) and comfort with data ...

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Quantitative Intern information

What is a quantitative intern?

Quantitative Interns are students or recent graduates who work in finance, technology, or research roles to apply mathematical, statistical, and programming skills to solve real-world problems. They often assist with data analysis, model development, and algorithm testing under the guidance of experienced quantitative analysts or researchers. These internships provide hands-on experience in quantitative fields such as investment banking, hedge funds, or tech companies, helping interns develop technical skills and industry knowledge. Through this role, interns gain exposure to complex financial instruments, coding languages like Python or R, and quantitative research methodologies.

What types of projects can a quantitative intern expect to work on during their internship?

As a Quantitative Intern, you can expect to work on projects involving data analysis, model development, and backtesting trading strategies. You may collaborate with senior quants and traders to analyze large datasets, validate models, and communicate your findings to the team. These projects often require strong programming skills and a proactive approach to problem-solving. The fast-paced environment offers opportunities to gain hands-on experience and exposure to real-world financial challenges.

What are the key skills and qualifications needed to thrive as a quantitative intern, and why are they important?

To thrive as a Quantitative Intern, you need strong mathematical, statistical, and analytical skills, typically supported by coursework in quantitative fields such as mathematics, statistics, computer science, or engineering. Familiarity with programming languages like Python, R, or MATLAB, and experience with data analysis tools and financial modeling software are commonly required. Attention to detail, problem-solving abilities, and effective communication are valuable soft skills in this role. These skills and qualities are crucial for accurately analyzing complex data sets, contributing to research, and delivering actionable insights in a fast-paced quantitative environment.

What is the difference between Quantitative Intern vs Quantitative Analyst?

AspectQuantitative InternQuantitative Analyst
Required CredentialsTypically pursuing or recent graduate with a degree in finance, mathematics, or related fieldBachelor's or master's degree in finance, mathematics, or related field; often requires some experience
Work EnvironmentInternship setting, often part-time or summer program within financial firms or investment banksFull-time role within financial institutions, hedge funds, or asset management firms
Employer & Industry UsageUsed by firms to train and evaluate potential future full-time analystsCore role responsible for developing models, analyzing data, and supporting trading decisions

In summary, a Quantitative Intern is an entry-level position aimed at gaining experience, while a Quantitative Analyst is a full-time professional responsible for quantitative modeling and analysis within the finance industry.

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Quantitative 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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