• Developed Python applications and PowerBI dashboards integrating SAP PM data, reducing process time by 20% and increasing overall operational efficiency. • Implemented system integrations between GitHub, SQL, and Jenkins, cutting manual tasks by 40% and enhancing data traceability by 50%. • Designed and deployed a machine learning model to optimize spare parts procurement, boosting predictive maintenance accuracy by 80%.

• Engineered ETL pipelines and predictive models (using PySpark, SQL) that increased vehicle price prediction accuracy by 75%. • Led a data migration from GCP to AWS and automated critical ETL processes, improving operational efficiency by 25%. • Developed an NLP model for WhatsApp to automate vehicle purchasing, reducing decisionmaking response time by 50% and increasing conversion rates by 15%.

• Spearheaded the development of machine learning models (TensorFlow, Scikit-Learn) for agro-climatic analysis, achieving a 60% improvement in prediction accuracy over a 25- year horizon. • Coordinated geospatial analysis projects and created

• Designed and implemented MILP optimization models with CPLEX-IBM, achieving a 30% reduction in operational costs. • Automated order management processes using Python and DB2-SQL, reducing process cycle time by 35% and increasing order throughput by 25%.

• Developed segmentation models (SVM, K-means) and performed behavioral analysis using Python, PostgreSQL, and MongoDB, leading to a 15% increase in targeted marketing efficiency. • Built interactive dashboards with Google Colab, Plotly, and QlikView that increased sales conversion rates by 20% and improved customer retention by 10%. • Streamlined data processing workflows, reducing data retrieval times by 40%.

• Created quantitative models and conducted financial analyses, reducing report generation time by 50%. • Automated daily reporting and index management with MATLAB/VBA libraries, increasing operational efficiency by 35%.