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Summer Quantitative Analyst Jobs (NOW HIRING)

Cornerstone Research

Summer Associate - MBA

Boston, MA · On-site +1

$7K/mo

Much of our work involves qualitative and quantitative analysis, including sophisticated ... Summer Associates help devise and apply innovative analytical approaches meeting the highest ...

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Summer Quantitative Analyst information

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$56.5K

$133.9K

$240K

How much do summer quantitative analyst jobs pay per year?

As of Jun 12, 2026, the average yearly pay for summer quantitative analyst in the United States is $133,877.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,500.00 and $145,500.00 per year, depending on experience, location, and employer.

What are Summer Quantitative Analysts?

Summer Quantitative Analysts are interns who work at financial institutions, such as investment banks or hedge funds, during the summer. Their primary role is to use mathematical models, statistical techniques, and programming skills to analyze financial data, assess risk, and help develop trading or investment strategies. These internships are typically aimed at students pursuing degrees in quantitative fields like mathematics, statistics, computer science, or engineering. The position offers hands-on experience with real-world financial problems and can often lead to full-time job offers after graduation.

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

To excel as a Summer Quantitative Analyst, you typically need strong quantitative and analytical skills, proficiency in statistics or mathematics, and enrollment in a relevant degree program such as finance, engineering, or computer science. Familiarity with programming languages like Python, R, or MATLAB, as well as experience with data analysis tools and financial modeling, is highly valued. Exceptional problem-solving ability, attention to detail, and effective teamwork and communication skills set candidates apart. These competencies are vital for analyzing complex data sets, developing models, and supporting data-driven decision-making in fast-paced financial environments.

What types of projects and responsibilities can a Summer Quantitative Analyst expect during their internship?

As a Summer Quantitative Analyst, you can expect to work on projects involving data analysis, model development, and back-testing trading strategies under the guidance of senior quants. You'll typically be tasked with analyzing large datasets, coding algorithms (often in Python, R, or C++), and presenting your findings to both technical and non-technical team members. Collaboration is a key part of the role, as you'll often work closely with traders, risk managers, and other analysts to refine models and implement solutions. The fast-paced environment provides hands-on exposure to real-world financial markets and quantitative methods, offering valuable learning and networking opportunities.
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Summer Quantitative Analyst jobs near you

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Posted 5 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)