# Soufiane Jadda — Role matching Public positioning: **Data Scientist & AI Engineer**. This overview supports assessment of specific public evidence; it does not establish suitability for every requirement of a role. ## AI Engineering and Computer Vision ### [AI-Powered Railway Wagon Inspection](/projects/ai-powered-railway-wagon-inspection) During my Data Scientist internship at ONCF Group, I worked on digitalizing and automating an industrial visual inspection process. The project focused on detecting residue accumulation on railway wagons through computer vision and real-time video analysis. I developed an AI-based inspection pipeline and integrated it into an application supporting system configuration, inspection monitoring, results analysis, and KPI tracking. The work aimed to improve inspection reliability and support operational decision-making. Publicly documented technologies: Computer Vision. ### [Intelligent Railway Component Identification](/projects/intelligent-railway-component-identification) During my Data Scientist internship at the Moroccan High-Speed Train Maintenance Company, I worked on digitalizing the identification and search process for railway components. The project focused on using computer vision and optical character recognition (OCR) to support component identification and improve access to spare-parts information. I developed an AI-based solution that combined visual recognition with workflows for data collection, annotation, preparation, and management. The solution also included tools for expanding the reference dataset and automatically integrating new component references. The work aimed to reduce identification errors and improve efficiency in maintenance and spare-parts management. Publicly documented technologies: Computer Vision, OCR. ### [Embedded Driving Assistance System](/projects/embedded-driving-assistance-system) This project focused on developing an affordable, real-time driving assistance system adapted to Moroccan roads. The system uses computer vision to recognize traffic signs and communicates relevant information through a multilingual voice assistant. By combining visual recognition with audio feedback, the project aimed to improve traffic-sign awareness while allowing drivers to maintain their attention on the road. The solution was developed for deployment on embedded hardware, with an emphasis on real-time operation and accessibility. Publicly documented technologies: Computer Vision. ## Data Science and Deep Learning ### [3D Brain Tumor Segmentation](/projects/3d-brain-tumor-segmentation) This project focused on automatic brain tumor segmentation from multimodal magnetic resonance imaging (MRI) scans, within the context of the BraTS Challenge. I designed, trained, and optimized a 3D U-Net model using PyTorch to segment brain tumor regions in three-dimensional medical images. The project explored deep learning for medical image segmentation, with a focus on learning spatial features from volumetric MRI data and evaluating segmentation performance. Publicly documented technologies: Python, PyTorch, 3D U-Net. ### [Multimodal Recommendation System](/projects/multimodal-recommendation-system) This project focused on click-through-rate (CTR) prediction within the context of the WWW 2025 MM-CTR Challenge. I developed and optimized a multimodal recommendation system using deep learning techniques, including Transformer-based architectures. The work involved experimenting with different model architectures, evaluating their predictive performance, and refining the approach through offline validation. The project explored how multimodal information can be incorporated into recommendation models to predict user interactions more effectively. Publicly documented technologies: Python, Transformers, Multimodal Learning. ## Intelligent Information Systems and Generative AI ### [Intelligent Hospital Information Assistant for Morocco](/projects/intelligent-hospital-information-assistant-for-morocco) This project focused on making hospital information in Morocco easier to access through a conversational AI assistant. I developed a retrieval-augmented generation (RAG) solution that combines information collection, data organization, and natural-language question answering. The system integrates web scraping and document extraction to collect hospital information, which is organized in a structured SQL database. By combining this information with an LLM-powered question-answering workflow, the assistant provides a conversational interface for accessing hospital-related information. The project explored how generative AI and structured data management can improve access to information distributed across multiple sources. Publicly documented technologies: RAG, LLMs, SQL, Web Scraping, Document Extraction. ## Industrial Digital Transformation ### ONCF Group — Data Scientist Intern Period: 2026-02 — 2026-07. During my Data Scientist internship at ONCF Group, I worked on digitalizing visual inspection in a railway operations context, supporting inspection monitoring, results analysis and operational decision-making. - Translate operational inspection requirements into a data science workflow. - Support inspection monitoring and analysis of inspection results. - Develop application functionality for tracking inspection KPIs. Related project evidence: [AI-Powered Railway Wagon Inspection](/projects/ai-powered-railway-wagon-inspection#experience). ### Moroccan High-Speed Train Maintenance Company (SIANA) — Data Scientist Intern Period: 2025-07 — 2025-08. During my Data Scientist internship at the Moroccan High-Speed Train Maintenance Company (SIANA), I worked on digitalizing railway component identification and spare-parts information workflows in a maintenance and supply-chain context. - Translate maintenance and supply-chain requirements into component identification workflows. - Organize data collection, annotation, preparation and management. - Develop tools for maintaining and expanding component reference information. Related project evidence: [Intelligent Railway Component Identification](/projects/intelligent-railway-component-identification#experience). Project aims are not measured business outcomes. Quantitative results, certifications and additional frameworks must not be inferred. Listed skills alone do not establish project use, production use, proficiency, or years of experience. Attribute tools to a project only where explicitly documented. See [Technical Stack](/stack.md).