NileSupply Chain Solutions is seeking a data scientist to develop a machine learning model that forecasts product demand across multiple warehouses and retail outlets. The goal is to optimize inventory levels, reduce stockouts, and minimize overstock situations by providing accurate, data-driven demand predictions.
Requirements:
Experience in time series forecasting and regression analysis
Strong grasp of supply chain operations and inventory dynamics
Proficiency in Python (Pandas, Scikit-learn, XGBoost, Prophet, etc.)
Ability to handle multi-store and multi-product datasets
Skills in feature engineering and model evaluation for demand forecasting
مراحل الوظيفة
Deliveries
Forecasting model with prediction horizon of 1–3 months
Clean and annotated Python code (Jupyter notebook preferred)
Plots comparing actual vs predicted sales
Performance metrics (e.g., RMSE, MAPE, accuracy by product line)