Nathalie Al makdessi

Data Scientist with 6+ years of broad-based experience building data-intensive applications, I have gained proficiency in machine learning, data processing, and data mining algorithms, as well as scripting languages such as Python and R. My experience as a teacher of data sciences has enhanced my ability to communicate complex concepts effectively.

 My experience spans multiple industries including academia, automotive and environmental research. I developed skills in machine learning, data processing and data mining algorithms, and scripting languages such as Python and R. My ability to effectively communicate complex concepts was honed through my work as a lecturer at ICAM Strasbourg and the Université Panthéon-Assas Paris 2, where I taught graduate data science courses, supervised MSc interns, and published research.

As an R&D Engineer at SP3H, I honed my data analysis skills by working with engineers to develop machine learning models of fuel consumption to optimize vehicle fuel characteristics. Additionally, my PhD at Irstea focused on developing machine learning algorithms for hyperspectral image analysis, demonstrating my ability to innovate and adapt.

I studied applied mathematics and then a doctorate in the same field. But what was relevant is when I was able to apply all my skills in data science training where I was able to develop the skills needed to build an application in different fields (health, e-commerce, .. .). I learned to perform multivariate statistical analyzes and to effectively communicate the results using clear and relevant graphical representations. I also acquire knowledge in performing univariate statistical analyzes and performing data cleaning operations on structured data. The training focuses on my ability to anticipate and meet consumer needs, as well as to segment customers on an e-commerce site. I also learned how to adapt hyperparameters for supervised and unsupervised learning algorithms, evaluate model performance and preprocess data for efficient modeling. Additionally, I learned how to deploy models via APIs, create scoring models, monitor model performance, and deploy models to the cloud. Finally, I familiarized myself with the main cloud service providers such as AWS, Azure or Google Cloud Platform (GCP) and their respective services.