Predictive Stock Level Optimization - 31/08/2026 09:23 EDT

Python, Machine Learning (ML), R Programming Language, Data Science, Data Analysis, Statistical Modeling, Predictive Analytics, Time Series Analysis · I have a full extract of our retail inventory history and I want to turn it into a living model that tells me exactly when, what, and how much to reorder so we stop tying up cash in slow-moving items while never running out of the fast movers. Your task is to dive into the inventory data, uncover the patterns that drive demand, and deliver a predictive engine focused on stock level optimization.

Here’s how I picture the engagement:

• Data assessment & preparation: explore the raw tables, flag gaps or anomalies, and structure the dataset so the model can consume it without manual fixes each cycle. • Model development: build and tune a demand-driven algorithm (time-series forecasting, probabilistic safety-stock calculations, or a hybrid you prefer) that outputs optimal reorder points and quantities per SKU, factoring seasonality, promotions, and supplier lead times. • Validation & iteration: stress-test accuracy with back-testing, explain any trade-offs between service level and inventory cost, and refine until the metrics hold up. • Deployment package: deliver clean, commented code (Python, R, or equivalent), a concise README, and a simple dashboard or set of visual reports