Nhan Nguyen
About Nhan Nguyen
Nhan Nguyen is a Data Engineer with a strong background in data analysis and statistics. He has worked for various organizations including SNCF, Mutuelle Mieux-Etre, and Famoco, and has a solid educational foundation in data science and applied mathematics.
Work at Papernest
Nhan Nguyen has been employed at Papernest as a Data Engineer since 2021. In this role, he applies his skills in data analysis and engineering to support the company's data-driven initiatives. His work involves utilizing programming languages and data science techniques to address various challenges within the organization.
Previous Experience in Data Analysis
Prior to his current position, Nhan Nguyen worked as a Data Analyst at Famoco for two months in 2020. He also held a position as a Data Analyst at SNCF for two months in 2019. Additionally, he served as Chargé d'études statistiques at Mutuelle Mieux-Etre from 2020 to 2021, where he focused on statistical studies for ten months in Ville de Paris.
Education and Expertise
Nhan Nguyen completed his M2 in Innovation, Marchés, and Science des données at Université Paris-Saclay from 2020 to 2021. He also earned a Diplôme d'ingénieur in Mathématiques appliquées and Science des données from ENSIIE - École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise between 2018 and 2021. His foundational education includes a Classe Préparatoire aux Grandes Écoles in Mathématiques, Physique, and Science de l'ingénieur at Lycée Wallon from 2015 to 2018.
Technical Skills and Programming Languages
Nhan Nguyen has developed expertise in several programming languages, including Python, R, and C. He utilizes various libraries such as pandas, scikit-learn, keras, and tensorflow for machine learning applications. His technical skills enable him to effectively address data-related challenges in his professional role.
Background in Data Science
Nhan Nguyen has gained substantial experience in data science through both his academic training and professional roles. He possesses a strong foundation in theoretical mathematical models applied to data science, which supports his ability to tackle complex data challenges in various settings.