* 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