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Internship Risk Engineer Jobs in Massachusetts (NOW HIRING)

Analogdevices

Manufacturing Engineer Intern

Chelmsford, MA · On-site

$17.50 - $22.50/hr

During your internship, you will make an impact through work on meaningful projects alongside a ... Aid in risk identification and mitigation with Design and Process Development engineers to support ...

Analog Devices

Manufacturing Engineer Intern

Wilmington, MA · On-site

$18.25 - $23.75/hr

During your internship, you will make an impact through work on meaningful projects alongside a ... Aid in risk identification and mitigation with Design and Process Development engineers to support ...

Analogdevices

Manufacturing Engineer Intern

Wilmington, MA

$18.25 - $23.75/hr

During your internship, you will make an impact through work on meaningful projects alongside a ... Aid in risk identification and mitigation with Design and Process Development engineers to support ...

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All JobsRisk Engineer JobsInternship Risk Engineer JobsInternship Risk Engineer Jobs in Massachusetts

Internship Risk Engineer information

What is the difference between Internship Risk Engineer vs Risk Engineer?

AspectInternship Risk EngineerRisk Engineer
Required CredentialsTypically pursuing or recently completed a relevant degree; internships may not require certificationsProfessional certifications like ASP, ARM, or PE often preferred
Work EnvironmentEntry-level, supervised, and learning-focused roles within companies or consulting firmsFull-time, experienced roles involving risk assessment, analysis, and mitigation strategies
Employer & Industry UsageUsed by companies during internship programs to train future risk professionalsUsed by industries such as insurance, construction, manufacturing, and energy for ongoing risk management

The main difference is that an Internship Risk Engineer is an entry-level, learning position often held by students or recent graduates, while a Risk Engineer is a full-time professional responsible for assessing and managing risks in various industries. Internships serve as a stepping stone toward becoming a full Risk Engineer with more responsibilities and certifications.

What are the most commonly searched types of Risk Engineer jobs in Massachusetts?

The most popular types of Risk Engineer jobs in Massachusetts are:

What are popular job titles related to Internship Risk Engineer jobs in Massachusetts?

For Internship Risk Engineer jobs in Massachusetts, the most frequently searched job titles are:

What job categories do people searching Internship Risk Engineer jobs in Massachusetts look for?

The top searched job categories for Internship Risk Engineer jobs in Massachusetts are:

What cities in Massachusetts are hiring for Internship Risk Engineer jobs?

Cities in Massachusetts with the most Internship Risk Engineer job openings:

Internship Risk Engineer jobs near you
Infographic showing various Internship Risk Engineer job openings in Massachusetts as of September 2026, with employment types broken down into 19% Internship, 66% Full Time, 6% Part Time, and 9% Contract. Highlights an 91% In-person, 5% Hybrid, and 4% 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 6 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)