Aziz Laabidi

• Developed and deployed a skill-based search filter within the internal application SMAC PEOPLE using Power Apps, integrating data automatically extracted from employees’ CVs through a Python script. • Automated document management processes and enabled dynamic updates of skill data in SharePoint using Power Automate, improving collaboration and visibility of internal expertise. • Analyzed limitations related to heterogeneous CV formats and proposed scalable solutions, including the use of Natural Language Processing (NLP) and enhanced search functionalities for broader deployment.

• Performed data cleaning and feature engineering to enhance model accuracy, addressing challenges related to large and complex datasets. • Implemented churn prediction models for 13 product categories using logistic regression, decision trees, random forests and SVMs, with logistic regression proving to be the most effective across all categories. • Identified key drivers of customer churn and retention, calculated potential revenue from retaining at-risk customers and proposed data-driven marketing strategies to improve customer loyalty for ADEO.

• Containerized Sobflous web application using Docker, ensuring consistent and portable deployments. • Implemented a CI/CD pipeline for testing, building and deployment processes using GitLab CI/CD. • Improved deployment efficiency and increased release frequency, significantly enhancing team productivity and overall delivery velocity.