• Project 1: Proficiently studied various factors influencing customer service levels like corporate parent, distance, transportation modes, Third Party Logistics (3PL), volume, regions, supply location and distribution channels• Successfully identified top factors influencing customer service levels using descriptives, correlation, and regression• Evaluated various bucketing strategies to cluster mile combinations to reduce data noise, finally considering the 125-mile bucket as a key factor in the linear regression model to find the On-Time Delivery (OTD) drop percentage with every 100 miles increase• Developed five linear regression models for On-Time Delivery, achieving over 70% R-squared value• Created a dashboard to calculate On-Time In Full (OTIF) fines using the OTD drop percentage derived from the regression model• Project 2: Developed On-Time Delivery (OTD) percentage prediction model using Python and employed machine learning (ML) models like logistic regression, neural networks, decision tree, random forest, and XGBoost • Performed data cleaning and removed outliers using statistical techniques like z-score and standard deviation thereby significantly enhancing prediction model accuracy, precision and recall value leading to more reliable results• Developed a prediction model using Random Forest Classifier, chosen for its superior accuracy above 80% across all temperature channels and further improved the model’s performance through hyperparameter tuning and predict probability function• Consulted with team on importance of relevant factors for the network optimization process based on feature importances obtained from different ML algorithms and ensured a data-driven approach for optimizing network performance• Developed a network analytics graphical user interface (GUI) in Python, designed to facilitate ease of use for individuals and enable comprehensive analysis of delivery performance through an accessible, user-friendly interface

• Performed quality testing of construction materials using ECTM, CBRTM, and CFTA to ensure material integrity and construction efficiency as per bill of quantity (BOQ) which led to better customer satisfaction.• Inspected structural quality assurance for bridge construction, ensuring integrity and safety with compliance to IS 456-2000, IS 10262, IRC:6-2017 codes and standard operating procedures (SOPs).• Coordinated with cross-functional teams for efficient construction planning through DPR, closely monitoring operational activities and conducted root cause analysis (RCA) to find out the delay in execution.• Monitored with site engineers and supervisors for streamlining workforce management and task distribution amongst 2 sub-contractors and 30 laborers which led to increased productivity.