Data Scientist
Current• Developed forecasting models for "Mother of Pearl," a terrazzo product, using time series analysis and machine learning techniques, contributing to enhanced inventory management and strategic decision-making.• Extracted and analyzed large, complex datasets to identify demand trends, employing Python and SQL for data querying, cleaning, and feature engineering.• Utilized Recursive Feature Elimination (RFE) and correlation thresholds for feature selection, ensuring robust model performance and generalizability.• Built and optimized multivariate forecasting models (including Multilayer Perceptron, Random Forest, LightGBM, CatBoost, and Gradient Boosting Machines) and univariate models (ARIMA, SARIMA, SARIMAX) through hyperparameter tuning, achieving a Mean Absolute Percentage Error (MAPE) of 9.16% (Development), 16.31% (In-time), and 10.30% (Out-of-time) with the optimized CatBoost model.• Collaborating closely with cross-functional teams to deploy models in a real-time system, improving efficiency and accuracy in predicting demand and managing inventory.