Nabih Nebbache
About Nabih Nebbache
Nabih Nebbache is a Data Engineer currently working at Contentsquare in Paris, France, with a background in software engineering and data science. He holds a Master's degree in Data Mining and Machine Learning from Université Lumière Lyon 2 and specializes in big data and machine learning technologies.
Work at Contentsquare
Nabih Nebbache has been employed at Contentsquare as a Data Engineer since 2021. In this role, he focuses on big data and web backend development, utilizing his expertise in various programming languages and frameworks. His work contributes to the company's data-driven decision-making processes and enhances user experience through data analysis.
Previous Experience in Data Science and Engineering
Before joining Contentsquare, Nabih Nebbache worked at Orange as a Data Scientist from 2020 to 2021. His role involved analyzing data to derive insights and support business strategies. He also completed a software engineering trainee position at AIR ALGÉRIE in 2017, where he gained foundational experience in software development.
Education and Expertise
Nabih Nebbache holds a Master's degree in Data Mining and Machine Learning from Université Lumière Lyon 2, which he completed from 2019 to 2020. He also studied at Ecole nationale Superieure d'Informatique (ESI) from 2014 to 2019. His technical expertise includes proficiency in Scala with Spark, Akka, Cats, and Playframework, as well as Python with Tensorflow, Keras, and Sklearn.
Machine Learning Projects and Skills
Nabih Nebbache has completed various machine learning projects, showcasing his skills in R programming language. He actively engages in the data science community by sharing his projects on GitHub under the username 'ThegreatShible'. His work reflects a strong commitment to advancing his knowledge and contributing to the field of data science.
Internship Experience in Deep Learning
In 2019, Nabih Nebbache undertook a six-month internship as a Deep Learning trainee at ECE-ESI in Paris. This experience allowed him to apply his theoretical knowledge in a practical setting, further developing his skills in deep learning technologies and methodologies.