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Internship Monte Carlo Simulation Jobs (NOW HIRING)

CMCOM LLC

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Senior Construction Management Project Risk Management Professional

Manhattan, NY · On-site

$70 - $120/hr

Perform quantitative risk modeling, including Monte Carlo simulation, cost-risk analysis, schedule-risk analysis, contingency modeling, and interpretation of risk results. * Support cost risk ...

CMCOM LLC

Be Seen First

Senior Construction Management Project Risk Management Professional

Manhattan, NY · On-site

$70 - $120/hr

Perform quantitative risk modeling, including Monte Carlo simulation, cost-risk analysis, schedule-risk analysis, contingency modeling, and interpretation of risk results. * Support cost risk ...

Peraton

Risk Analysis Engineer

Annapolis Junction, MD · On-site

$86K - $138K/yr

Implement Monte Carlo simulation engines for schedule risk analysis (SRA) and cost risk analysis * Design risk scoring models that combine traditional likelihood/impact matrices with NLP-detected ...

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How much do internship monte carlo simulation jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for internship monte carlo simulation in the United States is $15.54, according to ZipRecruiter salary data. Most workers in this role earn between $12.50 and $17.55 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Internship Monte Carlo Simulation, and why are they important?

To thrive in a Monte Carlo Simulation internship, you need a solid background in probability, statistics, and mathematical modeling, typically supported by coursework in mathematics, engineering, or computer science. Familiarity with programming languages such as Python, MATLAB, or R, and experience with simulation software are highly valued. Strong analytical thinking, attention to detail, and effective communication skills help interns interpret results and collaborate with team members. These competencies are crucial for accurately modeling complex systems, troubleshooting code, and translating findings into actionable insights.

What is an Internship Monte Carlo Simulation?

An Internship Monte Carlo Simulation is a training or work experience opportunity where interns use Monte Carlo simulation techniques to model, analyze, and solve complex problems involving randomness and uncertainty. Monte Carlo simulations use repeated random sampling to obtain numerical results and are widely used in fields like finance, engineering, science, and data analysis. During such an internship, participants may work on projects involving risk assessment, forecasting, optimization, or statistical analysis, often utilizing programming languages such as Python, R, or MATLAB. This experience helps interns develop practical skills in quantitative modeling, data analysis, and decision-making under uncertainty.

What are some typical projects or tasks an intern working with Monte Carlo simulation might encounter during their internship?

As an intern specializing in Monte Carlo simulation, you can expect to work on projects such as developing and running stochastic models to analyze risk, optimize processes, or predict outcomes in areas like finance, engineering, or operations. Your daily tasks may include writing code (often in Python, R, or MATLAB), analyzing simulation results, and presenting findings to team members. You'll likely collaborate closely with data scientists, analysts, and subject matter experts to refine models and interpret results. This hands-on experience will help you build both technical and communication skills, laying a strong foundation for future roles in quantitative analysis or data science.

What is the difference between Internship Monte Carlo Simulation vs Internship Data Analysis?

AspectInternship Monte Carlo SimulationInternship Data Analysis
Required SkillsProbability, statistics, programming (Python, R)Statistics, data manipulation, visualization
Work EnvironmentFinancial, insurance, risk modelingBusiness, marketing, research
Industry UsageRisk assessment, quantitative modelingData-driven decision making

Internship Monte Carlo Simulation focuses on using probabilistic models to assess risks and uncertainties, often in finance or insurance. In contrast, Internship Data Analysis involves interpreting data sets to inform business decisions. Both roles require strong statistical skills but differ in application and industry focus.

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Internship Monte Carlo Simulation jobs near you
Infographic showing various Internship Monte Carlo Simulation job openings in the United States as of May 2026, with employment types broken down into 50% Full Time, 25% Temporary, and 25% Contract. Highlights an 100% In-person job distribution, with an average salary of $32,333 per year, or $15.5 per hour.

AI System Analyst (AI Monte Carlo)

Saransh Inc

Houston, TX • On-site

Contractor

Posted 16 days ago

Job description

Role: AI System Analyst (AI Monte Carlo)
Client address: Houston, TX (Hybrid)
Contract
 
Experience Required: 10-12 years
 
Mandatory skills:
  • AI Monte Carlo
  • Python
     
Key Responsibilities:
  • Solution Architecture: Design and implement advanced Monte Carlo simulation frameworks to solve complex probabilistic problems (e.g., risk assessment, optimization, or predictive forecasting).
  • Client Engagement: Lead discovery sessions with clients to extract and define technical requirements from high-level business goals.
  • Cross-Functional Collaboration: Serve as the primary technical liaison between functional business units and core engineering teams to ensure alignment on deliverables.
  • End-to-End Delivery: Own the full lifecycle of AI development—from algorithmic design and data modeling to deployment and performance tuning.
  • Mentorship & Leadership: Provide technical guidance to junior/mid-level developers while maintaining the self-sufficiency to handle critical individual contributor tasks in agile environments.
Technical Qualifications:
  • Core AI & Math: Expert knowledge of Monte Carlo methods (MCMC, Sequential Monte Carlo, Quasi-Monte Carlo) and their application in AI/ML environments.
  • Programming: Mastery of Python or C++ (high-performance computing experience is a major plus).
  • Infrastructure: Solid understanding of cloud-based AI deployment (AWS, Azure, or GCP) and containerization (Docker/Kubernetes).
  • Strategic Thinking: 10+ years of experience navigating the trade