Freelancer profile translated to English.
Back to original languageAbout Ayman
Graduated engineer from ENSAE Paris.
What I can help with:
- Time series & forecasting: prediction, anomaly detection, uncertainty quantification. For example: deep learning forecasting models on large-scale data, predictions from open-access data, uncertainty estimates for financial risk assessment, ...
- Geospatial data & remote sensing: satellite imagery processing, spatio-temporal feature extraction, density analysis.
- Sensor & industrial data: peak detection, seasonality and anomaly analysis on production site streams
- Generative AI & agents: LLM integration and agent design to automate business tasks (RAG, orchestration, fine-tuning)
In parallel to my freelance missions, I am developing a deep tech project applied to the maritime transport sector, supported by an impact incubator.
Based in Lyon, I work fully remotely or in hybrid mode (Lyon / possible travel to Paris).
Have a complex data subject or an AI project to scope? Send me a few lines about your needs, and I'll get back to you quickly with an initial assessment!
French
Native or bilingual
Italian
Native or bilingual
English
Fluent
Can work on-site
Lyon (up to 20km), Paris (up to 20km)
Experience
- Galeries LafayetteData ScientistRETAIL (SMALL BUSINESS)July 2024 - December 2024 (5 months)Paris, France- Designed and implemented deep learning models for time-series forecasting- Processed and extracted predictive signals from large-scale and noisy real world datasets- Communicated predictive modeling results to engineering teams and decision-makers
- nista.ioData AnalystENERGY AND UTILITIESJune 2022 - August 2022 (2 months)Vienne, Austria- Built a Proof of Concept for industrial electricity peak detection, processing time-series sensor data from major manufacturing plants such as Alpla and Lafarge- Engineered visualizations that empowered energy domain experts to analyze complex seasonality patterns, pinpoint the exact origins of load anomalies, and extract actionable cost-saving strategies
- variate.energyData ScientistENERGY AND UTILITIESJuly 2023 - October 2023 (3 months)Berlin, Germany- Developed a cost-effective alternative to premium meteorological models to predict solar irradiance using open-access data.- Designed custom kernels to capture spatial, temporal, and periodic data structures, while providing crucial uncertainty estimations for financial risk assessment.- Validated the model across 25+ European locations, comparing its overall accuracy (RMSE) and systematic bias (RMBIAS) against standard spatial averaging methods.- Built a Proof of Concept for industrial electricity peak detection, processing time-series sensor data from major manufacturing plants such as Alpla and Lafarge- Engineered visualizations that empowered energy domain experts to analyze complex seasonality patterns, pinpoint the exact origins of load anomalies, and extract actionable cost-saving strategies
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Education
- Graduated engineer from ENSAE ParisENSAE Paris2024Spécialisation Data Science, Statistiques et Apprentissage
- Preparatory classes for Grandes ÉcolesLycée La Martinière Monplaisir2021Classes préparatoires aux grandes écoles - MPSI/MP*