AI/ML Data Scientist – Risk Analytics & Production

Moody's; Brussel Hoofdstad; Sur place; EUR 70 000 - 110 000; Plein temps

Résumé du poste

Moody's is seeking a data science professional to design, build, and deploy machine learning models and AI-powered workflows across Moody's Analytics. You will translate business problems into analytical approaches and measurable outcomes, explore large datasets, and collaborate with data engineers to productionize models.

The role emphasizes data quality, risk management, and efficient decision support, with responsibilities spanning experimentation, performance tracking, and clear

Qualifications

• Bachelor's or Master's in a quantitative field.
• 3+ years hands-on experience in machine learning, advanced analytics, or quantitative modelling.
• Experience deploying ML models and AI-powered workflows.
• Ability to translate business problems into analytical approaches and measurable outcomes.

Responsabilités

• Design, build, and deploy ML models and AI-powered workflows to improve data quality and decision-making across Moody's Analytics.
• Develop, test, and deploy ML models for defined business use cases.
• Translate business problems into analytical approaches, features, and measurable outcomes.
• Explore large datasets to surface trends, data quality issues, risks, and opportunities.
• Design experiments to validate models and measure business impact against success criteria.
• Collaborate with Data Engineers to productionize models through pipelines and monitoring.

Connaissances

AI concepts

Python

SQL

Statistics / ML

Git

Formation

Bachelor's or Master's in Data Science/CS/Math

3+ years ML/Analytics

Outils

Databricks

PyTorch

TensorFlow

AWS

Description du poste

Moody's is seeking a data science professional to design, build, and deploy machine learning models and AI-powered workflows across Moody's Analytics. You will translate business problems into analytical approaches and measurable outcomes, explore large datasets, and collaborate with data engineers to productionize models.

The role emphasizes data quality, risk management, and efficient decision support, with responsibilities spanning experimentation, performance tracking, and clear