Saad
Driouech

Fraunhofer IIS · Nuremberg, Germany · Working Student

• Training diffusion-based generative models for GNSS signal reconstruction under real-world interference conditions; iterating on architectures and loss functions to stabilise training and prevent mode collapse.
• Designed systematic evaluation frameworks measuring model robustness under noise and distribution shift; experimented with spectrogram and complex IQ data representations.
• Managed the full experimental lifecycle: hypothesis formulation, implementation, ablation studies, and documentation. Tracked all experiments with TensorBoard.
• Collaborated with signal processing engineers to translate experimental findings into concrete modelling decisions.

August-Wilhelm Scheer Institut · Saarbrücken, Germany · Working Student

• Built end-to-end ML pipelines for garment return prediction on cold-start products with no transaction history, achieving 86% balanced accuracy; addressed class imbalance and feature sparsity.
• Refactored and parallelized preprocessing pipelines, achieving a 5× runtime speedup and significantly improving iteration speed and reproducibility.
• Conducted feature importance analysis to identify key return drivers, enabling interpretable recommendations for business stakeholders.

Hightech Payment Systems · Casablanca, Morocco

• Enhanced PowerCARD, HPS's global payment switching and card management platform, to meet VISA and Mastercard compliance requirements.
• Worked with SQL databases, Docker containers, CI/CD pipelines, and Linux environments in a professional engineering setting.

Al Akhawayn University · Ifrane, Morocco · Part-time

• Pre-trained two transformer language models (DarELECTRA 52M, DarRoBERTa 80M) for low-resource Moroccan Darija on a 1 GB code-mixed corpus; fine-tuned on three downstream tasks.
• Text summarization: DarELECTRA achieved ROUGE-1 19.25 / ROUGE-L 18.01, state-of-the-art among all tested models including ARBERT and MARBERT.
• Topic classification: F1 0.84 / accuracy 0.86; offensive language detection: 90% accuracy / 85% F1; published at IEEE CiSt 2023 and MDPI 2024.

Wenov, Attijariwafa Bank Innovation Lab · Casablanca, Morocco

• Built a transformer-based intent classification system to automatically route multilingual client inquiries to relevant departments, replacing a legacy rule-based system.
• Outperformed traditional ML baselines by +7% accuracy / +4% F1; evaluated across multiple language variants and edge cases.