GenAI & Model Validation Summer Intern

Moody's; New York (NY); On-site; USD 41,000 - 55,000; Full time

Job summary

Moody's Quantitative Review (MRG) offers a 10-week summer internship to explore AI risk management within credit rating activities. The intern will assist in evaluating AI-enabled tools and quantitative models, with hands-on exposure to validation and governance practices under experienced validators.

The role emphasizes independent review, development of validation deliverables, and collaboration across the team, with mentorship and a focus on practical applications of model risk frameworks.

Qualifications

• Pursuing a quantitative or technical degree in a relevant field.
• Graduation window December 2027 – June 2029.
• Availability to work June 7 – August 13, 2027.
• Interest in AI, risk management, and model validation is desired.
• Experience with AI/GenAI concepts or coursework is a plus.

Responsibilities

• Assist with independent evaluation and challenge of AI-enabled tools, agents and quantitative models.
• Identify and test GenAI/agentic AI risks within model risk activities.
• Contribute to validation analyses: replication, sensitivity testing, benchmarking.
• Draft test plans and document results under guidance of validation lead.
• Help develop internal tools to streamline validation workflows.

Skills

Agentic AI

LLM Fundamentals

AI Eval

Model Risk

Credit Risk

Python

SQL

Education

Bachelors/Masters/Doctoral in CS/AI/ML/Math/Engineering

Graduation Dec 2027 – Jun 2029

Availability Jun 7 – Aug 13, 2027

Tools

Python

R

SQL

Job description

Moody's Quantitative Review (MRG) offers a 10-week summer internship to explore AI risk management within credit rating activities. The intern will assist in evaluating AI-enabled tools and quantitative models, with hands-on exposure to validation and governance practices under experienced validators.

The role emphasizes independent review, development of validation deliverables, and collaboration across the team, with mentorship and a focus on practical applications of model risk frameworks.