Med Nacer Cherni

• Customer Segmentation for a Retail Company: Conducted a data-driven analysis to identify key customer segments for a retail company. The segmentation was based on demographic, purchasing behavior, and sentiment analysis. The insights generated from this project were used to inform targeted marketing campaigns and personalize the customer experience. • Analyzed datasets and communicated insights to business managers to assist with data‑driven decision making. • Developed dashboards and reports that communicate a story and provide visualization of data in a way that can be best utilized. • Evaluated business processes and recommended data science solutions to improve efficiency. • Developed a similarity system to identify new errors introduced in the system, improving the matching process and handling their resolution as well as classifing them according to fixing complexity. Keywords : Python, Pandas, Numpy, Matplotlib, Deep Learning, Transformers, Bert, Flask, Rest API, streamlit, Automation

Recruitement Platform • Collaborated with various platform end‑users to analyze and determine their data requirements, as well as propose data solutions to optimize platform usage. • Developed a matching algorithm in Python that matches potential candidates to jobs on the platform to save time and accelerate the search process. • Created a resume parser and a linkedin scraper in Python that extracts candidates information from resumes or linkedin profiles in order to auto‑complete their account on the platform and thus speed‑up the fill process. • Deployed and documented all the developed solutions ( API’s ) on AWS EC2 instances and assisted the developers team in their integration process on the platform. Keywords : Python, NLP, REST API, Flask, Scrum, AWS, Deepl Learning, Human resource Renault Group • Identified valuable data sources and managed the collection process. • Prepared reports and data visualization in order to communicate insights to PMs and client members. • Created an automatic bug categorization system, reducing the time spent on bug processing, assigning them to the appropriate technical team and prioritizing them based on severity. • Developed a bug recommandation system in order to group them by maintenance topics. Keywords : Data Visualization, Python, NLP, TensorFlow, Transformers, REST API, Flask, Scrum, AWS, Deepl Learning, streamlit, Automotive Airbus • Developed a chatbot which aims to handle different types of NLQ tasks. • Analyzed and implemented research papers in the NLP field. • Trained and integrated state of the art models that handle Question Answering over text documents and SQL querying and Knowledge Graphs Databases. Keywords : NLP, Scrum, Transformers , Bert , LSTM, GRU , NetworkX , Chatterbot , Pytorch, Python, Aeronautics

Realization of a web application with the R programming language using the Rshiny library. The app allows data files to be imported and read so that statistical analyses can be performed on them, such as variable correlation and principal component analysis, to prepare them for the application of supervised and unsupervised learning methods.

Optimization of food production in the canteens. Analysis and prediction of products at sales and turnover per day/week/months/year and the develop an overview dashboard to facilitate decision-making