Data Scientist - Intermediate Level - Deadline 09/12/25

Job description

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