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How much do risk analytics intern jobs pay per hour?

As of Jul 29, 2026, the average hourly pay for risk analytics intern in the United States is $17.04, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $19.23 per hour, depending on experience, location, and employer.

What types of projects and data analysis tasks can a Risk Analytics Intern expect to work on during their internship?

As a Risk Analytics Intern, you can expect to work on projects that involve analyzing large datasets to identify, assess, and quantify risks within the organization. Typical tasks may include building and validating risk models, preparing reports for senior team members, and assisting in the development of dashboards to visualize risk metrics. Interns frequently collaborate with risk management, data science, and business teams to gather requirements and present findings. This hands-on experience helps you develop technical skills in data analysis and gain a better understanding of how risk is managed in a real-world business context.

What does a Risk Analytics Intern do?

A Risk Analytics Intern supports the risk management team by analyzing data to identify potential financial, operational, or strategic risks faced by an organization. Their typical responsibilities include collecting and cleaning data, running statistical analyses, preparing risk reports, and helping develop risk models. They may also assist in evaluating the effectiveness of current risk mitigation strategies and recommending improvements. This role provides valuable hands-on experience in data analysis, critical thinking, and exposure to risk management processes within a business environment.

What are the key skills and qualifications needed to thrive as a Risk Analytics Intern, and why are they important?

To thrive as a Risk Analytics Intern, you typically need strong quantitative and analytical skills, proficiency in statistics, and a background in finance, mathematics, or a related field. Familiarity with data analysis tools such as Excel, SQL, Python, or R, along with experience using risk management systems, is highly beneficial. Attention to detail, problem-solving abilities, and effective communication are important soft skills for interpreting data and presenting findings. These skills are crucial for accurately assessing financial risks and supporting strategic decision-making within the organization.

What is the difference between Risk Analytics Intern vs Risk Analyst?

AspectRisk Analytics InternRisk Analyst
Required CredentialsTypically pursuing or recent graduate in finance, economics, or related fieldsBachelor's or master's degree in finance, risk management, or related discipline
Work EnvironmentInternship programs, entry-level, supervised tasksFull-time professional role, independent analysis, decision-making
Employer & Industry UsageInternship positions in finance, banking, insurance, consultingFull-time roles in similar industries, more responsibility

The main difference between a Risk Analytics Intern and a Risk Analyst lies in experience, responsibilities, and employment status. Interns are typically students or recent graduates gaining introductory experience, while Risk Analysts are full-time professionals responsible for analyzing and managing risk strategies.

What cities are hiring for Risk Analytics Intern jobs? Cities with the most Risk Analytics Intern job openings:
What are the most commonly searched types of Risk Analytics jobs? The most popular types of Risk Analytics jobs are:
What states have the most Risk Analytics Intern jobs? States with the most job openings for Risk Analytics Intern jobs include:
Risk Analytics Intern jobs near you

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Re-posted 23 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)