Modeling and Quantitative Analytics Summer Intern

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Description du poste

At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence.

If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity. 

Skills and Competencies

  • Strong quantitative skills and experience in modeling. Experience may be gained through employment or academic study
  • Solid understanding of data engineering and data pipelines
  • Solid understanding of computer programming language such as python and SQL. Experience with Matlab is a plus
  • Attention to detail, pro-active and flexible
  • Excellent communication skills and team oriented
  • Knowledge of finance and economics a plus
  • Fluency in English is essential

Education 

  • Pursuing a Master’s or PhD degree in Finance, Financial Engineering, Mathematical Finance, Economics, Mathematics, Statistics, Physics, Engineering, Computer Science or related field
  • Graduation date of May 2027 – June 2028
  • Ability to work during program dates: June 15st - August 21th 2026

 Responsibilities

  • Design and develop automation systems to streamline our credit risk model development and monitoring processes, from building data pipelines to pull data from databases, calibrating and re-calibrating models, to evaluating performance of models
  • Collaborate across different business lines and stakeholders to ensure online delivery of the development work
  • Research econometric, statistical and mathematical techniques to evaluate performance of econometric models
  • Prepare documentation and present to senior leadership on the development progress
  • Assist with quantitative projects to update and improve existing credit rating models and scorecards
  • Participate in professional training for the analytical staff

About the team

Our Modeling & Quantitative Analytics team is responsible for developing and maintaining quantitative credit risk and credit rating models for use in our rating methodologies. Our work enables the company to provide ratings to numerous entities in the capital markets, from corporates to securitized products. By joining our team, you will be part of exciting work in model development and enabling the company to provide ratings to the market, as well as a dedicated team that adheres to the top-notch modeling standards and shares an excellent culture.

Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law.

Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody’s Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.

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  • Affiché : 11/17/2025
  • Référence du poste #: 10762
  • Niveau d'expérience: Early Career
  • Secteur d'activité: CSS(CSS)
  • Catégories:
    • Students & Early Careers
  • Emplacement(s):
    • 5 An der Welle, Frankfurt am Main Hessen