A Data Scientist with a Ph.D. in Chemical Engineering, specializing in data science, with substantial experience in the enhancement of neural networks through the creation of novel mathematical functions for weight optimization and neuron pruning. Co-supervision of academic theses has focused on the development of computer vision applications and virtual analyzers. Proficient in Python, PySpark, SQL, and Power BI, with extensive use of libraries such as Pandas, Numpy, SciPy, StatsModels, Matplotlib, Seaborn, Plotly, scikit-learn, TensorFlow, PyTorch, Optuna, PyCaret, MLflow, DVC, and pytest to translate innovative ideas into actionable insights. Proven track record in collaborative environments has yielded significant advancements in predictive modeling and process enhancement, particularly within people analytics. Implementation of machine learning processes for turnover prediction employing classification, regression, and clustering techniques has facilitated strategic decision-making by elucidating root causes and identifying at-risk groups, thereby reducing turnover rates across diverse organizational contexts.