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

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How much do computer science intern jobs pay per hour?

As of Sep 15, 2026, the average hourly pay for computer science intern in Lowell, MA is $20.17, according to ZipRecruiter salary data. Most workers in this role earn between $15.54 and $24.82 per hour, depending on experience, location, and employer.

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.

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Infographic showing various Computer Science Intern job openings in Lowell, MA as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 81% Full Time, 13% Part Time, 3% Contract, and 1% Nights. Highlights an 72% Physical, 1% Hybrid, and 27% Remote job distribution, with an average salary of $41,957 per year, or $20.2 per hour.

PhD Graduate Intern Quantitative Portfolio Risk Analytics

Cambridge, MA • On-site

RiskAnalytics
See more jobs at RiskAnalyticsexternal

Other

Re-posted 13 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)

Apply