Xai Data Scientist Researcher | Python, Flask, Spark, Machine Learning, Data Analysis, Scikit-Learn
CurrentAs a Research Data Scientist at the ALGORITMI Center, I specialize in Explainable Artificial Intelligence (XAI) within the Supply Chain domain. My work involves developing and deploying machine learning models to address various challenges, such as demand forecasting and supplier delay prediction.Key Achievements and Skills:Machine Learning Models: Conceptualized and implemented predictive models using algorithms such as random forests, gradient boosting, and neural networks to achieve up to 0,17% NMAE accuracy in demand forecasting.Supplier Delay Prediction: Designed models to predict supplier delays, reducing unexpected delays.XAI Techniques: Incorporated SHAP, LIME, ALE, and VEC to create interpretable models, improving stakeholder trust and model transparency.Tools and Technologies: Proficient in Python, Flask, Apache Spark, Scikit-learn, and Apache Hive for data processing, analysis, and model deployment.Data Processing: Transformed and managed large datasets with Spark and Hive, ensuring efficient and scalable data workflows.Created insightful visualizations using libraries such as Matplotlib and Seaborn to communicate findings powerfully.