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Intern Quantitative Modeling Jobs (NOW HIRING)

Akuna Capital

Quantitative Research Intern

Chicago, IL · On-site

$130K/yr

What you'll do as a Quantitative Research Intern at Akuna: Akuna's Quant team is looking to add ... Develop quantitative models describing market behavior. * Advance existing initiatives and explore ...

TransMarket Group

Algorithmic Trader Intern

Chicago, IL · On-site

Description As an Algorithmic Trader Intern, you will have the opportunity to learn and experience ... Build and maintain trading quantitative model tools and analytics * Learn and master programming ...

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Intern Quantitative Modeling information

What are the key skills and qualifications needed to thrive as an intern quantitative modeling?

To thrive as an Intern in Quantitative Modeling, you need a solid foundation in mathematics, statistics, and programming, often supported by coursework in quantitative fields like finance, engineering, or computer science. Familiarity with tools such as Python, R, MATLAB, and experience with statistical modeling libraries or financial data systems is highly valuable. Strong analytical thinking, attention to detail, and effective communication skills distinguish top candidates in this role. These skills and qualities are crucial for accurately analyzing complex data, building reliable models, and clearly presenting findings to support business or research decisions.

What is an intern quantitative modeling?

Intern Quantitative Modeling positions are internship roles where students or recent graduates work with experienced quantitative analysts to develop and implement mathematical models that assist in financial decision-making, risk management, or trading strategies. Interns typically use statistical, programming, and analytical skills to analyze large data sets and help solve real-world financial problems. These internships offer hands-on experience with quantitative tools, exposure to industry practices, and opportunities to learn from professionals in fields such as finance, insurance, or technology.

What is the difference between Intern Quantitative Modeling vs Intern Data Analysis?

AspectIntern Quantitative ModelingIntern Data Analysis
Required SkillsMathematics, programming, financial modelingData manipulation, statistical analysis, visualization
Work EnvironmentFinance, investment banks, hedge fundsTech companies, consulting firms, research institutions
Typical TasksBuilding models, forecasting, risk assessmentData cleaning, reporting, trend analysis

Intern Quantitative Modeling and Intern Data Analysis roles often overlap in skills like programming and data handling. However, quantitative modeling focuses more on creating financial or mathematical models used for decision-making, while data analysis emphasizes interpreting data to uncover insights. Both roles are common in finance and tech industries, but they serve different strategic purposes.

What does an intern quantitative modeling do?

As an Intern in Quantitative Modeling, you can expect to assist with developing, testing, and validating mathematical models used for financial analysis, risk assessment, or trading strategies. Typical tasks include working with large datasets, coding in programming languages such as Python or R, and collaborating with senior analysts or data scientists to solve real-world business problems. You'll often participate in team meetings, present your findings, and receive feedback to refine your models. This hands-on experience will help you build strong technical and analytical skills, while giving you exposure to the collaborative nature of quantitative teams in finance or technology firms.
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Intern Quantitative Modeling jobs near you

PhD Graduate Intern Quantitative Portfolio Risk Analytics

RiskAnalytics

Cambridge, MA • On-site

Other

Posted 8 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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