Freelancer profile translated to English.
Back to original languageAbout Sahnoun
Data Scientist & Machine Learning Engineer – Specialized in designing predictive models, creating intelligent chatbots and AI agents, and developing Power BI decision-making dashboards that enable companies to better analyze, anticipate, and automate their processes.
French
Native or bilingual
English
Fluent
Can work on-site
Paris (up to 50km)
Experience
- GofieldAI Agent for Intelligent Audit Checklist Generation (Retail)CONSULTING AND AUDITSFebruary 2025 - July 2025 (5 months)Tunis, Tunisia– Designed an AI agent that generates store-specific audit checklists by leveraging each store’s audit history, non-compliance patterns, and corrective actions.– Built a multi-agent orchestration combining rule-based constraints with contextual signals (store profile, category, seasonality, recent issues).– Developed a RAG pipeline using CamemBERT embeddings, FAISS, MongoDB, and an Ollama-based LLM to ground recommendations in internal documents and past audits.– Integrated structured audit reports, templates, and field feedback into standardized, actionable checklist outputs.– Implemented a feedback/learning loop with Stable-Baselines3 PPO and a custom OpenAI Gym environment to improve checklist prioritization and relevance.– Standardized store data and agent interfaces via the Model Context Protocol (MCP) to ensure interoperability and easier integration.Impact: Improved audit compliance across stores and increased the consistency and actionability of generated checklists.
- Tunis Stock ExchangeStock Market PredictionJune 2024 - August 2024 (2 months)Tunis, Tunisia– Extracted and parsed high-frequency time-series from MDG servers using robust regex-based pipelines and automated checks.– Built a reproducible preprocessing workflow (cleaning, encoding, scaling, temporal alignment) with leakage-safe, time-based splits.– Implemented and compared deep forecasting models (RNN, LSTM, DeepLOB) with walk-forward evaluation and ablation testing.– Improved performance through systematic hyperparameter tuning (sequence length, hidden size, dropout, learning rate) and model calibration.Impact: Achieved 60% accuracy on short-term trend prediction, improving the reliability of analyst monitoring.
- ADHEPACKEmployee Turnover PredictionRAW MATERIALS INDUSTRYJune 2023 - July 2023 (1 month)Sfax, Tunisia– Prepared data through cleaning, encoding, and scaling to ensure optimal integration with machine learning models.– Built a complete pipeline using ColumnTransformer and Pipeline to streamline data transformations and model predictions.– Compared multiple classification models including Logistic Regression, Decision Tree, Random Forest, and XGBoost, using metrics such as accuracy, precision, and recall.– Developed a user interface with Tkinter enabling real-time prediction of employee turnover risk.
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Education
- MSc Data Management & AIEcole Centrale d'Electronique et Informatique Paris (ECE Paris)2025
- Data ScientistHigher School of Statistics and Information Analysis Tunis2025Engineering
Certifications
- Generative AI and LLMs: Architecture and Data PreparationIBM2024
- Gen AI Foundational Models for NLP & Language UnderstandingIBM2024