Pule Mothobi

I work at the intersection of wholesale automotive operations and data analytics, combining hands-on supply chain experience with a growing focus on predictive decision tools.While many analysts encounter logistics through clean datasets, I’ve experienced it from the receiving dock — where lead times slip, SKU discrepancies appear, and inventory risk becomes tangible. That operational perspective shapes how I approach data: ground truth is not only in databases, but in the physical flow of goods and the constraints around them.I specialize in translating raw logistics and procurement data into practical insights that improve visibility, reduce uncertainty, and support better buying decisions. By combining my experience as a Receiving Clerk with an intermediate foundation in machine learning and automation, I focus on building tools that connect operational reality with analytical models.Current project work includes:• Inventory Risk Detection Tool — a predictive analytics system that evaluates SKU-level financial exposure by combining demand variability, unit cost, and lead time factors to identify potential overstock, dead stock, and stockout risks.• Shipment Tracking & Monitoring System — a structured tracking framework designed to improve inbound visibility, detect delays early, and support operational planning through centralized shipment intelligence.Where I add value:Information Insight — moving beyond simple price comparison toward data-supported costing, supplier evaluation, and total cost awareness.Predictive Operations— applying machine learning and statistical analysis to forecast demand patterns and detect anomalies in inventory movement.Risk Awareness— developing analytical approaches to quantify supplier and logistics uncertainty so decisions are informed rather than reactive.I don’t just understand the data — I understand the environment the data comes from. I am particularly interested in applying Python, SQL, and machine learning to real-world problems across supply chains, procurement, and automotive systems.Tech Stack: Python (NumPy, Pandas), SQL, Scikit-Learn, TensorFlow/Keras, Data Visualization, Automation (n8n)I’m open to connecting with professionals working in supply chain analytics, operations intelligence, and applied machine learning.