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Freelance Probabilistic Risk Analysis Engineer Jobs in Massachusetts

Investment Risk Analyst Pioneer Investments is a diversified asset management firm and a key ... Technical & Programming * Proficiency in Python (pandas, numpy) for data analysis and automation

Investment Risk Analyst

Boston, MA ยท On-site

$75K - $90K/yr

Investment Risk Analyst Pioneer Investments is a diversified asset management firm and a key ... Technical & Programming * Proficiency in Python (pandas, numpy) for data analysis and automation

Conduct quantitative cost and schedule risk analysis. (QCRA / QSRA) * Prepare project and/or ... Bachelor's Degree in Engineering, Construction Management, Economics, Finance or relevant field ...

Conduct quantitative cost and schedule risk analysis. (QCRA / QSRA) * Prepare project and/or ... Bachelor's Degree in Engineering, Construction Management, Economics, Finance or relevant field ...

A Systems Analysis Engineer develops and applies advanced modeling, simulation, and mission ... risk, and operational trade spaces across domains including strategic deterrence, missile defense ...

A Systems Analysis Engineer develops and applies advanced modeling, simulation, and mission ... risk, and operational trade spaces across domains including strategic deterrence, missile defense ...

A Systems Analysis Engineer develops and applies advanced modeling, simulation, and mission ... risk, and operational trade spaces across domains including strategic deterrence, missile defense ...

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Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA โ€ข On-site

Full-time

Posted 19 days ago


Job description

Ph.D. Graduate Intern โ€“ 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 applyโ€”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 approachesย 
  • Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of riskย 
  • Design and implement analytical tools and pipelines using Python and SQLย 
  • Contribute to model validation, backtesting, and performance evaluationย 
  • Collaborate with risk, engineering, and data teams to improve model scalability and data infrastructureย 
  • Communicate complex quantitative insights through clear visualizations and technical summariesย 
  • Apply advanced methodologies from your discipline (e.g., stochastic modeling, optimization, machine learning, or geometric/topological approaches) to improve risk analyticsย 
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)ย 
  • Strong foundation in probability, statistics, and numerical methodsย 
  • Proficiency in Python (NumPy, pandas, or similar) and/or SQLย 
  • Experience working with large datasets and implementing quantitative modelsย 
  • Ability to think rigorously about complex systems and translate theory into practical solutionsย 
Preferred Qualifications
  • Familiarity with quantitative finance concepts (e.g., portfolio theory, factor models, volatility modeling, Value-at-Risk)ย 
  • Experience with scientific computing, optimization, or machine learningย 
  • Background or research in cross-disciplinary areas such as:ย 
    • Statistical physics, complex systems, or network theoryย 
    • Applied or computational mathematicsย 
    • Machine learning or probabilistic modelingย 
    • Quantum computing or advanced optimization techniquesย 
    • Topological data analysis or geometric data methodsย 
  • Prior research, publications, or project work demonstrating advanced quantitative modelingย 
What Youโ€™ll Gain
  • Exposure to real-world portfolio risk problems at the intersection of finance and advanced analyticsย 
  • Opportunity to apply cutting-edge academic methods in a production environmentย 
  • Collaboration with a highly quantitative, cross-disciplinary teamย 
  • Experience working with large-scale financial data and modern analytics infrastructureย 
  • Mentorship and potential pathway to full-time quantitative rolesย 
Duration & Compensation
  • Internship: Summer 2026, with potential to extendย 
  • Paid internship (competitive, based on experience and location)
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