clarissa.cabral.leite

Clarissa Cabral Leite bridges academic research and practical machine learning engineering through her work in data science and Python development. Her technical portfolio centers on data pipeline orchestration using Mage and Airflow frameworks. She contributes actively to open-source machine learning initiatives including ML-Zoomcamp and MLOps projects. Her development work focuses on predictive modeling and workflow automation within Jupyter Notebook environments. She implements machine learning engineering practices across research and production contexts. Her projects demonstrate applied experience in data pipeline architecture and model deployment. Her background combines advanced academic credentials in scientific research with hands-on technical expertise. She specializes in translating research methodologies into practical machine learning applications. Her work synthesizes data science fundamentals with software engineering principles.