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Computer Science Intern Jobs in Stoughton, MA (NOW HIRING)

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Computer Science Intern information

What is a computer science intern?

A Computer Science Intern is a temporary position where students or recent graduates gain hands-on experience in software development, data analysis, cybersecurity, or other computing fields. Interns typically assist with coding, debugging, research, and testing while working under the guidance of experienced professionals. This role helps build technical skills, industry knowledge, and networking opportunities, preparing interns for full-time positions in the tech industry.

What kind of projects and tasks can I expect as a computer science intern?

As a Computer Science Intern, you can expect to work on a variety of tasks such as writing and testing code, debugging software, supporting ongoing development projects, and collaborating on team-based assignments. Interns often contribute to designing features, fixing bugs, conducting research, or assisting in quality assurance under the guidance of senior engineers or mentors. The exact nature of your projects may vary based on the company and team, but you will typically gain hands-on experience with real-world software development tools and workflows. This exposure not only builds your technical skills but also helps you understand agile work environments and best practices, making it a valuable step toward a full-time role in the field.

What are the most commonly searched types of Computer Science jobs in Stoughton, MA?

The most popular types of Computer Science jobs in Stoughton, MA are:

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Cities near Stoughton, MA with the most Computer Science Intern job openings:

Computer Science Intern jobs near you
Infographic showing various Computer Science Intern job openings in Stoughton, MA as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 79% Full Time, 16% Part Time, 2% Contract, and 1% Nights. Highlights an 81% Physical, 2% Hybrid, and 17% Remote job distribution.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Cambridge, MA • On-site

Risk Analytics Company
See more jobs at Risk Analytics Companyexternal

Full-time

Re-posted 7 days ago

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

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)