Data Scientist - Intermediate Level - Deadline 09/1...

• Good understanding of machine learning techniques and statistical methods, such as classification, clustering, and basic predictive modelling.

• Experience developing analytics with Python or R, using common libraries and frameworks.

• Ability to apply quantitative methods to practical, real-world problems.

• Familiarity with concepts like risk scoring, anomaly detection, and reducing bias in models.

• Basic knowledge of modern data science tools and environments (e.g., RStudio, Anaconda, Git/version control).

• Awareness of model governance principles such as reproducibility and interpretability.

• Ability to explain technical results clearly to non-technical audiences.

• Capacity to work independently on analytics tasks while aligning with team and project needs.

• Comfortable working in international and multicultural environments.

• Strong teamwork and adaptability.

• Ability to manage multiple tasks and priorities.

• Willingness to participate in multilingual meetings.

• Excellent communication skills in English (written and spoken); knowledge of French is a plus.

• High level of integrity when handling sensitive information.

• Around 4 years of hands-on experience in applying data science models in a professional context.

• Practical experience building statistical or machine learning models, with applications such as fraud detection, risk classification, or predictive analytics.

• Exposure to the full lifecycle of model development — from preparing data to testing and deployment.

• Ability to contribute to structured analytics within a cross-disciplinary team.

• Background in academic projects or applied research is considered an advantage.

Level: Intermediate

Deadline 09/12/25