Finance Predictive Analytics Model Development

I’m preparing a predictive analytics initiative for a finance-focused dataset stored in traditional relational tables and well-formatted spreadsheets. The core objective is to turn this structured historical information into forward-looking insights—think probability scores for default, churn, or other key risk and revenue indicators. You will take ownership of the full machine-learning pipeline: data ingestion from CSV/SQL sources, exploratory analysis, feature engineering, model selection and training, validation, and a concise performance report. I expect reproducible Python code (preferably using pandas, scikit-learn, XGBoost or similar), clear comments, and a short write-up that explains both the methodology and how to refresh the model with new data. Deliverables: • Clean, well-documented source code and notebooks • Trained model artifact plus inference script or API endpoint • Brief technical report covering data prep, chosen algorithms, metrics, and next-step recommendations Success is measured by robust cross-validated accuracy, interpretability suited for finance stakeholders, and easy handoff so I can run or retrain the solution without hassle. If you have a proven track record combining AI/ML with structured financial data, I’m ready to move quickly.