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Internship Model Validation Jobs (NOW HIRING)

Intel

Mixed Signal validation engineer

Santa Clara, CA · On-site

$122K - $172K/yr

... internship experiences and or schoolwork/classes/research. Minimum Qualifications: * Bachelors ... Work Model for this Role This role will be eligible for our hybrid work model which allows ...

Intel

Mixed Signal validation engineer

Folsom, CA · On-site

$122K - $172K/yr

... internship experiences and or schoolwork/classes/research. Minimum Qualifications: * Bachelors ... Work Model for this Role This role will be eligible for our hybrid work model which allows ...

Leggett & Platt

Systems Engineer Intern

Detroit, MI · On-site

$16.50 - $21.50/hr

We offer internships in fields ranging from engineering, business, finance, operations ... Exposure to systems engineering principles (requirements, V-model, validation). * Familiarity with ...

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Internship Model Validation information

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$22

$51

$78

How much do internship model validation jobs pay per hour?

As of Aug 21, 2026, the average hourly pay for internship model validation in the United States is $52.00, according to ZipRecruiter salary data. Most workers in this role earn between $39.42 and $63.22 per hour, depending on experience, location, and employer.

What is an internship in model validation?

An Internship in Model Validation is a temporary position, usually for students or recent graduates, where you assist in reviewing and testing financial or statistical models used by organizations. Interns help ensure these models are accurate, reliable, and compliant with regulations. Tasks often include analyzing data, running simulations, and documenting findings under the supervision of experienced model validators. It's an excellent way to gain hands-on experience in quantitative finance, risk management, or data science. These internships are valuable for building technical and analytical skills relevant to careers in finance, banking, or consulting.

What types of projects or tasks can I expect to work on during an internship in model validation?

As an intern in model validation, you will typically assist in evaluating and testing financial or statistical models to ensure their accuracy and compliance with regulatory standards. Your daily tasks may include reviewing model documentation, running validation tests, analyzing model outputs, and preparing reports for senior analysts. You'll often collaborate with model developers, risk managers, and other stakeholders to discuss findings and suggest improvements. This hands-on experience provides valuable exposure to quantitative methods, regulatory frameworks, and cross-functional teamwork within the financial industry.

What are the key skills and qualifications needed to thrive as an internship model validation, and why are they important?

To thrive in a Model Validation Internship, you need a solid foundation in quantitative disciplines such as mathematics, statistics, finance, or computer science, often supported by progress toward a relevant degree. Familiarity with statistical software or programming languages like Python, R, or MATLAB, and knowledge of financial modeling systems, is highly valuable. Strong analytical thinking, attention to detail, and clear communication skills set standout candidates apart. These skills and qualities are crucial for accurately assessing model performance, ensuring compliance, and effectively presenting findings to technical and non-technical stakeholders.

What is the difference between Internship Model Validation vs Data Analyst?

AspectInternship Model ValidationData Analyst
Required CredentialsTypically pursuing or recent graduate in finance, economics, or related fieldsBachelor's degree in statistics, mathematics, or related field; certifications optional
Work EnvironmentInternship setting within financial institutions or banks, supervised by senior staffOffice-based, analyzing data sets, creating reports, often in various industries
Employer & Industry UsageUsed in banking, finance, and risk management for model validation tasksUsed across industries for data analysis, reporting, and decision support

Internship Model Validation focuses on assessing the accuracy and compliance of financial models during an internship, often within banking or finance sectors. Data Analysts perform broader data interpretation and reporting tasks across industries. While both roles involve data skills, Model Validation internships emphasize financial model testing, whereas Data Analysts handle diverse data analysis functions.

What cities are hiring for Internship Model Validation jobs?

Cities with the most Internship Model Validation job openings:

What are the most commonly searched types of Model Validation jobs?

The most popular types of Model Validation jobs are:

What states have the most Internship Model Validation jobs?

States with the most job openings for Internship Model Validation jobs include:

Internship Model Validation jobs near you
Infographic showing various Internship Model Validation job openings in the United States as of July 2026, with employment types broken down into 9% Internship, 1% As Needed, 68% Full Time, 20% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 1% Hybrid, and 12% Remote job distribution, with an average salary of $108,152 per year, or $52 per hour.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Re-posted 15 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)