Data Scientist

Within the Digital Finance team, the Data Sciencist will be responsible for developing machine learning solutions for high-profile data science initiatives. The Data Scientist 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.
A successful candidate will demonstrate an excellent knowledge of the various steps of a machine learning project, including modeling and MLOps. The candidate should also be innovation-minded, result-oriented, autonomous, and able to deliver finished products, sometimes under short deadlines.
The data science team works closely with technology teams, rating and research teams, and other departments. The candidate should therefore be able to collaborate across multiple divisions and communicate clearly and understandably to business representatives unfamiliar with data science.
The Data Sciencist will also strive to meet Moody s values: openness, diversity, inclusivity, respect, and a willingness to learn.
The duties of the Data Sciencist include:
Select and train machine learning models for predictive analytics, sometimes with relatively small and unbalanced datasets.
Build solutions predicting activities and extracting signals from multiple data sources.
Design explainability tools understandable by non-data scientists.
Collaborate with tech teams to create data ingestion pipelines connected to sources spread across different parts of the organization and delivered in varying formats.
Communicate results to business stakeholders and decision-makers.
Collaborate with subject matter experts from ratings and research teams to incorporate fundamental expertise into machine learning models.
Stay current with the latest research and technology developments.
Qualifications
Master s degree in data science, computer science, statistics, mathematics, or a related quantitative field.
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