MARJAN IZADPANAH

I am a dedicated Data and Business Analyst with a solid foundation in Operations Management and Industrial Engineering. My expertise lies in turning complex data into actionable insights that streamline operations and inform strategic decisions. Known for meticulous data analysis and model building, I also bring a commitment to data integrity and accuracy. I effectively translate technical details into clear strategies, engaging stakeholders across various levels.

RAILROAD TRANSPORTATION OF DANGEROUS GOODS IN CANADA: DATA-DRIVEN RISK ANALYSIS AND EMERGENCY MANAGEMENT, Granted by 2022 Social Science and Humanities Research Council (SSHRC), St. Catharine’s, ON, CA, 06/01/22, 05/01/24, Leveraged MySQL to retrieve historical weather data and topographical information through API services. Employed Python to analyze over 5,700 train incidents from 1999 to 2023, deriving key insights into factors influencing hazardous material releases. Utilized Excel to generate pivot tables, enhancing feature selection processes. Applied Python for comprehensive data preprocessing, including data balancing, scaling, and managing missing entries. Implemented advanced machine learning models using Python, including Logistic Regression, Decision Trees, and Neural Networks with two hidden layers. Conducted hyperparameter tuning and evaluated model performance using confusion matrices and feature importance graphs. STRATEGICALLY ALLOCATION OF NON-WOVEN FABRIC RESOURCES, ESSENTIAL FOR FACIAL MASK PRODUCTION, DURING THE PANDEMIC, Baftineh and Baftsan Company, Tehran, Iran, 05/01/20, 12/01/21, Utilized MS Access to integrate and manage diverse datasets, including historical infection rates, fabric production capacity, inventory, and logistics constraints. Employed Excel to clean and prepare data, using pivot tables and complex formulas to seasonally adjust and analyze infection rate trends across 28 provinces in Iran. Developed interactive dashboards and geographic maps using Tableau for 67 sales regions and approximately 320 facial mask manufacturing sites, facilitating effective assignment and transshipment amidst logistic constraints. Regularly presented detailed performance reports to the managerial team every two to three days, enabling data-driven decision making.