Solal V.

Data Scientist

Synaps

Dec 2021 – May 202317 months

We envision a world in self-sovereign identity where individuals are truly in charge of their identity and can decide what part of their ID they want to disclose.

Partnership - Event Manager/ Jassim Association

ASSOCIATION JASSIM

May 2018 – May 202248 months

The purpose of the association is to study and promote all actions and initiatives aimed at preventing accidents in high mountains, analyse their causes and develop the spirit of responsibility and discernment of users, practitioners and actors of high mountains. All our members are united around the motto: "combine freedom and responsibility", a motto that resonates through our actions combined with high mountain rescue services and the various personalities who are sensitive to our convictions. The Jassim association will continue its action for a safer and more responsible mountain.

Data Scientist

Banque de France

Sep 2020 – Dec 202115 months

Recommendation System (WEBSTAT Data): Developed a recommendation system leveraging over 100,000 time series to optimize decision-making and insights. Applied advanced statistical modeling and machine learning techniques to enhance predictive accuracy. Economic Uncertainty Indicator (Twitter & NLP): Built an economic uncertainty indicator using Twitter data and NLP. Leveraged BERT-based transformer models for sentiment analysis and topic classification, fine-tuning them to detect economic signals from real-time social media trends.

Intern Remote Sensing / Machine learning and Deep Learning in geosciences

SPACEBEL

May – Sep 20204 months

Internship within the Geospatial information unit, Spacebel : High-Resolution Remote Sensing (UAV Imagery): Processed UAV imagery for advanced remote sensing analysis, enhancing spatial resolution and feature extraction for environmental and urban studies. Tree Crown Detection & Species Classification: Developed a computer vision pipeline for detecting tree crowns and classifying species using convolutional neural networks (CNNs). Optimized model performance for high-resolution aerial imagery, improving accuracy in forestry and ecological monitoring.