• 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.

Specific Expertise

• 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