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Internship Stochastic Modeling Jobs (NOW HIRING)

Raytheon

RF Data Link Engineer II

Dallas, TX

At RTX, our internships, co-ops and full-time careers provide an exceptional foundation to work on ... Develop, prototype, and test data link models and communication signal processing algorithms in ...

Raytheon

RF Data Link Engineer II

Tucson, AZ

At RTX, our internships, co-ops and full-time careers provide an exceptional foundation to work on ... Develop, prototype, and test data link models and communication signal processing algorithms in ...

Raytheon

RF Data Link Engineer II

Los Angeles, CA

At RTX, our internships, co-ops and full-time careers provide an exceptional foundation to work on ... Develop, prototype, and test data link models and communication signal processing algorithms in ...

Raytheon

RF Data Link Engineer II

Albuquerque, NM · On-site

At RTX, our internships, co-ops and full-time careers provide an exceptional foundation to work on ... Develop, prototype, and test data link models and communication signal processing algorithms in ...

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Internship Stochastic Modeling information

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How much do internship stochastic modeling jobs pay per hour?

As of Jun 6, 2026, the average hourly pay for internship stochastic modeling in the United States is $17.31, 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 daily tasks can I expect during an internship in stochastic modeling?

As an intern in stochastic modeling, you will typically work on projects involving data analysis, model development, and simulation of random processes. Your daily tasks may include cleaning and organizing datasets, implementing mathematical models using programming languages like Python or R, and running simulations to test hypotheses. You will likely collaborate with senior modelers and data scientists, participate in team meetings, and may also present your findings. This role offers exposure to real-world applications in industries such as finance, insurance, or engineering, helping you develop both technical and communication skills.

What is the difference between Internship Stochastic Modeling vs Data Analyst?

AspectInternship Stochastic ModelingData Analyst
Required CredentialsRelevant coursework, basic programming skillsBachelor's in statistics, data science, or related field
Work EnvironmentInternship setting, research-focused, financial or tech industriesOffice environment, various industries including finance, marketing, healthcare
Employer & Industry UsageFinancial firms, tech companies, research institutionsCorporations, consulting firms, government agencies

Internship Stochastic Modeling typically involves applying probability and statistical techniques to model uncertainty, often in finance or research settings. Data Analysts focus on interpreting data, creating reports, and supporting decision-making across diverse industries. While both roles require analytical skills, stochastic modeling internships emphasize mathematical modeling, whereas data analyst roles focus on data manipulation and visualization.

What are Internship Stochastic Modeling positions?

Internship Stochastic Modeling positions are temporary roles designed for students or recent graduates to gain practical experience in applying stochastic processes and mathematical modeling techniques. Interns typically work under the guidance of experienced professionals in fields such as finance, insurance, engineering, or data science. They assist with analyzing data, developing models that incorporate randomness or uncertainty, and supporting decision-making processes. These internships provide valuable hands-on experience and help interns build skills in programming, statistical analysis, and problem-solving relevant to stochastic modeling careers.

What are the key skills and qualifications needed to thrive as an Internship Stochastic Modeling, and why are they important?

To thrive in a Stochastic Modeling Internship, you need a solid background in probability, statistics, and mathematics, often supported by coursework or a degree in a quantitative field. Familiarity with programming languages such as Python, R, or MATLAB, and experience using statistical modeling software are typically expected. Strong analytical thinking, attention to detail, and effective communication are crucial soft skills for interpreting data and explaining complex concepts. These abilities ensure you can develop accurate models, collaborate with teams, and contribute valuable insights to data-driven decision-making processes.
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Internship Stochastic Modeling jobs near you
Infographic showing various Internship Stochastic Modeling job openings in the United States as of May 2026, with employment types broken down into 98% Full Time, and 2% Contract. Highlights an 83% Physical, 6% Hybrid, and 11% Remote job distribution, with an average salary of $35,995 per year, or $17.3 per hour.

Ph.D. Graduate Intern Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA

Full-time

Posted 29 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 applyespecially 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 Youll 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)