DeFi and Digital Assets Intern
Trouvé dans: Talent FR C2 - il y a 2 semaines
The intern will be assigned to the team for the 6-month internship. Availability to start in Q1 January 2024
Role/Responsibilities
Moody’s Digital Finance team covers the analytical and operational aspects of emerging technologies and risks, including artificial intelligence, blockchain, digital assets, and cyber security.
Within the Digital Finance team, the intern will support the AI Analytics group in developing machine learning solutions for high-profile data science initiatives. The intern will notably assist in designing and training predictive models 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 key steps of a machine learning project, including data engineering and modeling. The candidate should also be innovation-minded, result-oriented, and able to deliver finished products, sometimes under short deadlines.
The data science team will sometimes work with technology teams, rating and research teams, and other departments. The candidate should, therefore, demonstrate a willingness to collaborate across multiple divisions and, on occasion, communicate clearly and understandably to business representatives unfamiliar with data science.
The intern will also strive to meet Moody’s values: openness, diversity, inclusivity, respect, and a willingness to learn.
A successful intern can aspire to receive an offer to join Moody’s after graduation.
The duties of the intern include:
- Participate in the selection and training of machine learning models for predictive analytics, sometimes with relatively small and unbalanced datasets.
- Build solutions predicting financial metrics and extracting signals from multiple data sources.
- Assist in the design of 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.
- Collaborate with subject matter experts from ratings and research teams to incorporate fundamental expertise into machine learning models.
- Graduation date expected in 2024.
Qualifications
- Students working towards a Master’s degree or Ph.D. in data science, computer science, statistics, mathematics, or a related quantitative field.
- Robust knowledge of machine learning algorithms and principles.
- Good understanding of cloud capabilities.
- Deep understanding of the tools explaining machine learning predictions.
- Expertise in Python and SQL.
- Knowledge of natural language processing.
- Good communication and presentation skills, with the ability to explain complex analytical concepts to people from other fields.
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