Summary
Sharone Boubli is a Senior Machine Learning Engineer with 8+ years’ experience building production ML and deep learning systems for recommendation, intent modeling, anomaly detection and video understanding. Based in Paris, she has led technical roadmaps and served as the go-to expert for insights and recommendation safety at Contentsquare and Dailymotion, shipping models that balance diversity, consistency and user frustration signals. Her background blends strong research foundations (MSc from ENS Paris-Saclay) with hands-on engineering—implementing LSTM autoencoders, transformer-based early intent predictors and multi-modal video pipelines in TensorFlow. An educator and community builder, she teaches probability & statistics, leads the Women in Big Data Paris chapter and mentors interns, evidencing a talent for translating research into product impact. Notably, she routinely combines sequential deep learning and anomaly detection to surface early user intent signals—a niche that drives both UX insights and commercial metrics.
8 years of coding experience
7 years of employment as a software developer
Diplôme d'ingénieur Ingénierie Mathématique Informatique, Diplôme d'ingénieur Ingénierie Mathématique Informatique at École des Ponts ParisTech
Bachelor of Applied Science (B.A.Sc.) Applied Mathematics to Programming and Economics, Bachelor of Applied Science (B.A.Sc.) Applied Mathematics to Programming and Economics at Université Paris Dauphine - PSL
MSc in Machine Learning and Computer Vision MVA Mathematiques Vision Apprentissage, MSc in Machine Learning and Computer Vision MVA Mathematiques Vision Apprentissage at École Normale Supérieure Paris-Saclay
French, English, Spanish, Hebrew, Arabic