Wissem Chabchoub

Wissem Chabchoub has had a varied work experience, beginning in 2017 at Goaland as a Data Scientist, where they developed a PoC of a Machine Learning model for Anomaly Detection in retail type databases, designed a Duplicate Record Detection algorithm, and planned and supervised project integration. In 2018, they worked as a Research Intern at IM2NP. In 2019, they worked at Crédit Mutuel Arkéa as a Data Scientist, where they designed a Deep Neural Network for Anomaly Detection in payment servers and analysed Symbolic Time Series. In 2020, they worked at Worldline Global as a Data Scientist, where they built a Deep Neural Network for stock returns modeling, performed sentiment analysis on textual data using NLP methods, and implemented a portfolio optimization algorithm based on Genetic Algorithms. Currently, they are working at SESAMm as a Quantitative Researcher.

Wissem Chabchoub has a well-rounded education history. From 2017 to 2020, they attended Centrale Lille and received a Master of Engineering in Finance and Data Science. Wissem then attended University of Paris I: Panthéon-Sorbonne from 2020 to 2021, where they obtained a Master of Science in Applied Mathematics. Before that, they attended Tunisia Polytechnic School from 2016 to 2020, where they received a Master of Engineering in Finance and Data Science. In addition to their formal education, they have obtained several certifications, including CFA Level 1 from the CFA Institute in 2022, IELTS from Cambridge Assessment in 2020, Financial Engineering and Risk Management Part I from Columbia University in 2019, Neural Networks and Deep Learning from deeplearning.ai in 2019, TOEIC from ETS in 2019, Machine Learning A-Z from Udemy in 2018, and MOOC Gestion de Projet from MOOC Gestion de Projet in 2017.