I’m part-way through a financial data analysis pipeline that starts in SQL and finishes in Python, and I need an expert who can take it over the finish line. The relational database already holds several years of transaction-level records; the core tables and basic queries exist, but they still need refinement so the data that lands in Python is fully analysis-ready. Once the dataset is solid, the main objective is to build and validate predictive models that forecast key financial metrics. I’m comfortable running your code once it’s delivered, but I’m looking for someone who can handle everything from feature engineering through model tuning and evaluation. Typical tools in my stack are PostgreSQL, pandas, scikit-learn and Jupyter, so familiarity with those (or comparable libraries) is essential. Deliverables I’m expecting: • Polished SQL scripts that extract and aggregate the required features • A well-commented Python notebook or .py script that trains, tests and saves the predictive model(s) • Brief documentation outlining assumptions, model performance, and how to reproduce the results If you have a track record of turning raw financial data into accurate forecasts, I’d love to see how you’d approach this.