Data Scientist / Machine Learning Engineer

Developed a clustering-based proof of concept to identify potentially fraudulent sales patterns from transactional data. Built and deployed a sales forecasting pipeline across SKU, store, and country levels using Azure Data Foundry. Designed scalable data workflows integrating preprocessing, modeling, and automated updates for business reporting. Implemented agentic AI workflows using n8n for automated content generation leveraging LLMs. Automated internal business processes using Power Automate and Azure Logic Apps, improving operational efficiency. Collaborated with cross-functional teams in an international consulting environment to deliver ML-driven solutions.

Developed deep learning models for audio and image classification, and time series forecasting using TensorFlow and PyTorch. Designed and implemented end-to-end ML pipelines, including data preprocessing, feature engineering, model training, and validation. Evaluated model performance using statistical metrics and guided optimization for real-world deployment. Advised on research projects, contributing to methodology design, experimental setup, and results interpretation.

Monitored and evaluated the status of the KM3NeT positioning subsystem (electronic compass system). Developed Python-based tools for data monitoring, validation, and analysis. Built data pipelines to support continuous analysis and research workflows. Collaborated with multidisciplinary international teams to identify and solve technical issues. Presented weekly progress and results through structured reports and presentations. Participated in 4 conferences (2 national, 2 international).

Coordinated weekly meetings, including agenda planning and documentation of key outcomes. Tracked progress on group objectives and maintained structured reports.

Conducted technical reviews of internal research documentation and datasets. Gained experience working with scientific databases and experimental data.