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Summer Environmental Risk Jobs in Massachusetts (NOW HIRING)

Dana-Farber Cancer Institute, Inc.

$31K - $41K/yr

We strive to create an inclusive, diverse, and equitable environment where we provide compassionate ... risk and underserved populations. We conduct groundbreaking research that advances treatment, we ...

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All JobsSummer Environmental Risk JobsSummer Environmental Risk Jobs in Massachusetts

Summer Environmental Risk information

What is the difference between Summer Environmental Risk vs Summer Environmental Technician?

AspectSummer Environmental RiskSummer Environmental Technician
CertificationsEnvironmental certifications, such as OSHA HAZWOPEREnvironmental certifications, such as OSHA HAZWOPER
Work EnvironmentFieldwork, site assessments, risk analysisFieldwork, sampling, data collection
Employer & IndustryEnvironmental consulting firms, government agenciesEnvironmental consulting firms, government agencies

Both roles often require similar certifications and work in outdoor environments. However, Summer Environmental Risk focuses on assessing and managing environmental hazards, while Summer Environmental Technicians primarily perform sampling and data collection tasks. Understanding these differences helps clarify career paths and job expectations in the environmental industry.

What are the most commonly searched types of Environmental Risk jobs in Massachusetts? The most popular types of Environmental Risk jobs in Massachusetts are:
What cities in Massachusetts are hiring for Summer Environmental Risk jobs? Cities in Massachusetts with the most Summer Environmental Risk job openings:
Summer Environmental Risk jobs near you

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Re-posted 25 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)