In my previous role, I developed machine learning solutions to forecast sales and revenue time-series for big international clients. My responsibilities included training, validating, and testing machine learning models, with the interpretation of relative metrics using Python. I also conducted data pre-processing, including data cleaning, visualization, feature extraction, and selection, and presented objectives to stakeholders. To manage databases, I used SQL, Google Data Studio, Google Cloud, and AWS. Additionally, I applied feature selection algorithms to models predicting outcomes of interest, such as sales, and analyzed data sets to identify trends or patterns. To improve model accuracy and facilitate collaboration among team members, I tracked ML experiments using MLflow. I researched novel solutions to tackle existing problems through literature reviews. Furthermore, I have experience using Git for version control and collaboration. To deploy and manage machine learning models in a production environment, I utilized Docker containers and RESTful APIs. I am familiar with common data science libraries and frameworks such as NumPy, Pandas, Scikit-learn, TensorFlow, and Keras.

My Education

Take a look at my formal education

Master of Science in Telecommunication EngineeringPolitecnico di Milano | 2016

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