Aziz Ghadhoun

• • Engineered an automated ML Forecasting System using multi-model ensembles (Random For est, Prophet, LSTM) to predict revenue metrics with an R2 score of 0.92.

• • Developed a scalable microservices architecture using FastAPI for ML operations and Sym fony 6 for real-time visualization, reducing forecast preparation time by 88%.

• • Implemented an automated ETL pipeline for data processing, handling high-variance datasets through time series cross-validation.

• • Selected as one of 50 fellows from 455 applicants (Top 10%) for intensive civic-tech training.

• • Specialized in Product Management, effective coding/testing, and Data Governance.

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