I am currently a Ph.D. candidate in Computer Science at the Gran Sasso Science Institute (GSSI) in Italy, graduating in November, specializing in temporal graphs and machine learning. My research combines theoretical computer science with practical machine learning applications, focusing on developing novel graph neural network (GNN) architectures for analyzing temporal networks. This experience has given me strong skills in designing and implementing scalable data science pipelines capable of extracting insights from large, dynamic datasets. In my recent work, I have developed temporal graph neural networks using Python and PyTorch, with custom attention layers and training objectives to handle complex time-evolving data. These skills are directly applicable to real-world data science challenges involving time series, logs, and metrics. Beyond research, I have solid programming expertise in Python, C++, and Julia, along with experience managing databases (SQL). I am also familiar with Docker and cloud environments, making me comfortable with deploying and maintaining data-driven applications. Working on temporal GNNs has sharpened my ability to create end-to-end machine learn- ing pipelines that address evolving data dependencies—skills essential for building reliable, production-ready data science solutions. I am enthusiastic about expanding my expertise in AI and applying it to practical projects that improve data-driven decision making, system reli- ability, and interoperability. What excites me about this Data Engineer role is the opportunity to leverage my strong algo- rithmic background and machine learning expertise to extract actionable insights from complex datasets and build models that drive real business impact. I am particularly motivated by chal- lenges that require integrating advanced AI techniques into existing data infrastructure and by collaborative environments where data science contributes directly to product improvement.…I am currently a Ph.D. candidate in Computer Science at the Gran Sasso Science Institute (GSSI) in Italy, graduating in November, specializing in temporal graphs and machine learning. My research combines theoretical computer science with practical machine learning applications, focusing on developing novel graph neural network (GNN) architectures for analyzing temporal networks. This experience has given me strong skills in designing and implementing scalable data science pipelines capable of extracting insights from large, dynamic datasets. In my recent work, I have developed temporal graph neural networks using Python and PyTorch, with custom attention layers and training objectives to handle complex time-evolving data. These skills are directly applicable to real-world data science challenges involving time series, logs, and metrics. Beyond research, I have solid programming expertise in Python, C++, and Julia, along with experience managing databases (SQL). I am also familiar with Docker and cloud environments, making me comfortable with deploying and maintaining data-driven applications. Working on temporal GNNs has sharpened my ability to create end-to-end machine learn- ing pipelines that address evolving data dependencies—skills essential for building reliable, production-ready data science solutions. I am enthusiastic about expanding my expertise in AI and applying it to practical projects that improve data-driven decision making, system reli- ability, and interoperability. What excites me about this Data Engineer role is the opportunity to leverage my strong algo- rithmic background and machine learning expertise to extract actionable insights from complex datasets and build models that drive real business impact. I am particularly motivated by chal- lenges that require integrating advanced AI techniques into existing data infrastructure and by collaborative environments where data science contributes directly to product improvement.WWWW…