I’m ready to build a brand-new predictive analytics solution that draws on our operational performance data to surface actionable insights and forecasts. The goal is to move beyond descriptive reports and give the business a forward-looking view of KPI trends, potential bottlenecks, and resource requirements. Scope of work • Ingest structured and semi-structured operational performance data (SQL tables and CSV exports). • Design, train, and fine-tune a model—Python is mandatory, and I’m comfortable with TensorFlow or PyTorch if that’s the most efficient route. • Package the model behind a lightweight API or script so it can be triggered on demand and return clear, explainable forecasts. • Provide concise documentation covering data pipeline, feature engineering, and model interpretability. Acceptance criteria The delivered model must outperform our current baseline (simple moving average) by a statistically significant margin on a held-out test set, include SHAP or comparable explainability metrics, and run end-to-end in under five minutes on a mid-range server. Clean code, clear commit history, and a brief handover call will complete the engagement.