Cloud Architect / Business Analyst

• Involved in the migration of DigitalOcean's infrastructure to AWS,designing solutions with cloud engineers.
• Utilized and optimized AWS solutions ( Computing, Storage , Networking).
• Managed deployments of back-end via GitLab, with new releases manually updated on VMs in accordance with client agreements.
• Ensured the security access to ressources and created scalable, resilient, available ressource infrastructure.
• Managed SQL, NoSQL Data bases (MySQL, SQL Server, Amazone Aurore, MongoDB and Apache Cassandra) .
• Carried out continuous integration using GitLab's testing functions.
• Followed a manual process for continuous delivery to respect client workflows ( Backend and Frontend Code versions).
• Participated in migrations and collaborations with clients such as PIC, Metlife, Aviva, Phoenix, Just, and SCOR.

G-Research Crypto Forecasting

I forecasted the values of 14 major cryptocurrencies by developing predictive models from scratch. I utilized Python for data retrieval and preprocessing and created recurrent neural networks (RNNs) to predict cryptocurrency values. Leveraging TensorFlow and Keras, I developed innovative prediction methods and tested them to minimize errors. I validated these methods on real-time data and evaluated results over time, applying skills in Keras, Python, predictive modeling, artificial intelligence (AI), big data, TensorFlow, deep learning, and Apache Spark.

Data Assimilation Intern

During my internship at the Institut de Mathématiques de Toulouse, I analyzed Covid-19 data and collected and preprocessed data from various countries. I developed a regression model and a statistical SIRD (Suspected, Infected, Recovered, Death) model using the implicit Euler method to predict health states. I also used a neural network to predict mortality and recovery rates, leveraging my skills in Keras, Python, recurrent neural networks, crisis management, TensorFlow, mortality analysis, and epidemiology.

Data Scientist

During the Defi IA 2022 with MeteoNet and INSA Toulouse, I utilized MeteoNet data and my expertise in Python to predict daily precipitation. I preprocessed and cleaned data to make it usable, and created a recurrent neural network (RNN) to forecast future precipitation. I adapted the model to the data format for accurate future predictions, leveraging my skills in artificial intelligence (AI), statistical data analysis, and predictive modeling.

Mathematics Research Intern

I delved deeply into the inverses of Drazin and their generalizations, mastering their theory and applications in algebraic structures. I developed expertise in extending traditional inverse concepts and linking spectral theory to algebraic properties. I applied these advanced concepts to solve complex Markov chain problems, enhancing my understanding of their dynamics. My passion for pure mathematics and functional analysis drove me to utilize and implement generalized inversibility techniques to tackle challenging issues.

Linear Regression and Analysis of Variance

During my internship at LAGA, I passionately applied linear regression and ANOVA to uncover insights and evaluate statistical differences. Mastering descriptive statistics and ARMA models, I forecasted and understood complex data trends. With proficiency in R for advanced analysis and Excel for data visualization, I rigorously validated hypotheses and expertly modeled time series to reveal hidden patterns and cyclical behaviors.