Data Scientist Intern - Digital Finance

Moody's; Paris; Sur place; EUR 100 000 - 125 000; Plein temps

Résumé du poste

A global leader in risk assessment is seeking a Data Scientist Intern in Paris to develop machine learning solutions for high-profile data science initiatives. The role includes designing predictive models, collaborating across teams, and converting data-driven insights into business actions. Candidates should be pursuing a Master's degree in a relevant field and possess skills in machine learning, Python, and data communication.

Qualifications

• Currently pursuing a Master’s degree in data science, computer science, statistics, mathematics, or a related field.
• Internship should serve as an end-of-study placement.

Responsabilités

• Select and train machine learning models for predictive analytics.
• Build solutions predicting activities from multiple data sources.
• Design explainability tools for non-data scientists.
• Communicate results to business stakeholders.

Connaissances

Machine learning experience

Python

SQL

Natural language processing

Excellent communication skills

Knowledge of Git

Formation

Master’s degree in data science or related field

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

• Experience in machine learning, with a strong knowledge of algorithms and principles
• Proven track record of successfully modeling, building, and putting in production machine learning applications
• Deep understanding of the tools explaining machine learning predictions
• Expertise in Python and SQL
• Knowledge of Git and collaboration principles
• Proven experience in natural language processing
• Excellent communication and presentation skills, with the ability to explain complex analytical concepts to people from other fields
• Previous experience in corporate finance or debt markets is preferred but not required

Education

• Currently pursuing Master’s degree in data science, computer science, statistics, mathematics, or a related quantitative field
• Internship should serve as an end-of-study placement

Responsibilities

Within the Digital Finance team, the Data Scientist Intern will contribute to the development of machine learning solutions for high-profile data science initiatives. The Data Scientist Intern will notably focus on designing and training predictive analytics models, crafting signals from unstructured data and creating high-value-added solutions converting quantitative predictions into actionable insights for the business.

The intern will gain exposure to Moody’s data science workflows and collaborate with cross‑functional teams across finance, technology, and research.

Responsibilities include:

• Selecting and training machine learning models for predictive analytics, sometimes with relatively small and unbalanced datasets
• Building solutions predicting activities and extracting signals from multiple data sources
• Designing explainability tools understandable by non‑data scientists
• Collaborating with tech teams to create data ingestion pipelines connected to sources spread across different parts of the organization and delivered in varying formats
• Communicating results to business stakeholders and decision‑makers
• Collaborating with subject matter experts from ratings and research teams to incorporate fundamental expertise into machine learning models
• Staying current with the latest research and technology developments