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Risk Analyst Internship Jobs (NOW HIRING)

Credit Agricole CIB

Bachelor's degree in Finance or related field Prior internships in credit analysis Strong credit ... risk exposure and sizing Ability to analyze and summarize information Ability to prioritize ...

Kennedy Wilson

RISK MANAGEMENT ANALYST

Beverly Hills, CA · On-site

$38.46 - $43.27/hr

Support the management of the summer internship program, including recruiting coordination ... Risk Management industries * Strong analytical skills and attention to detail * Proficient in ...

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How much do risk analyst internship jobs pay per year?

As of Aug 15, 2026, the average yearly pay for risk analyst internship in the United States is $108,333.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,000.00 and $131,000.00 per year, depending on experience, location, and employer.

What is a risk analyst internship?

A Risk Analyst Internship is a temporary, entry-level role where interns assist in identifying, analyzing, and mitigating financial, operational, or strategic risks within an organization. Interns typically work with senior analysts to assess risk exposure, develop risk models, and contribute to reports or recommendations. They may use data analysis, market research, and risk management tools to support decision-making. This role provides hands-on experience in risk assessment and exposure to financial regulations, compliance, and industry best practices.

What are the key skills and qualifications needed to thrive in the risk analyst internship position, and why are they important?

To thrive as a Risk Analyst Intern, you typically need a background in finance, economics, statistics, or a related field, along with strong analytical and quantitative skills. Familiarity with data analysis tools such as Excel, SQL, and statistical software (like R or Python) is often required, as well as knowledge of risk assessment frameworks. Excellent attention to detail, communication skills, and the ability to collaborate effectively in teams are valuable soft skills for this position. These skills ensure accurate evaluation of potential risks and support collaborative problem-solving in a fast-paced business environment.

What types of projects or tasks can a risk analyst intern expect to work on?

As a Risk Analyst Intern, you can expect to assist with gathering and analyzing data to identify potential risks, preparing reports for senior team members, and supporting the team in developing risk mitigation strategies. Projects may include examining financial models, researching industry trends, or evaluating compliance with regulatory standards. You'll likely collaborate closely with experienced analysts and other departments such as finance or compliance, gaining exposure to a variety of real-world business challenges. This hands-on experience provides valuable insights into risk management and helps build a strong foundation for a career in the field.

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Risk Analyst Internship jobs near you
Infographic showing various Risk Analyst Internship job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 8% Part Time, and 3% Contract. Highlights an 88% Physical, 5% Hybrid, and 7% Remote job distribution, with an average salary of $108,333 per year, or $52.1 per hour.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Re-posted 10 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)