Mohamed Aimen Zebouchi

Developed a scientific computing pipeline using Python, C++, and SQL to aggregate and structure large multi-source datasets for risk assessment. Performed exploratory data analysis to process complex signals and identify key risk drivers and statistical trends.

Designed a Python-based platform for modeling and analyzing complex data frameworks and transforming raw inputs into structured algorithmic decision-making models. Implemented interactive reasoning pipelines to support high-performance data interpretation and scenario analysis.

Worked on a master final project analyzing customer behavioral data to build a digital twin model for predicting subscriber reactions and improving ROI of value-added service marketing campaigns. Created and automated interactive tracking dashboards to transform data and support KPI monitoring in near real time. Segmented customer databases and delivered data-driven recommendations for business development, cross-selling, and retention.

Collaborated with multiple departments to understand internal workflows and operating processes at enterprise scale. Built relationships across teams and documented insights gained from observing real-world operational activities to strengthen cross-department understanding.

Built operational monitoring dashboards to track infrastructure health KPIs and identify anomalies for proactive management decisions. Focused on turning complex data into clear visual insights and worked with cross-functional IT teams to support better real-time system performance.