I’m looking for a data scientist based anywhere in Latin America to help me create reliable predictive models for a finance-focused project. You’ll start with large historical datasets stored in SQL and deliver models that accurately forecast key financial indicators. I work mainly with Python, so you’ll find Pandas, NumPy, Scikit-learn and, when deep learning is justified, TensorFlow already in place. If you prefer R for certain tasks, that’s perfectly fine as long as the final workflow remains reproducible. The end-user needs to consume insights through Power BI, so once the model is validated I’ll ask you to craft intuitive dashboards that highlight drivers, confidence ranges and any red-flag anomalies the model detects. Solid statistical grounding is essential; I want clear explanations of feature importance, assumptions and limitations that business stakeholders can grasp quickly. Big-data exposure, cloud familiarity (Azure, AWS or GCP), ETL pipeline design and MLOps practices are all welcome extras—you’ll have room to propose improvements if they make the solution more robust or scalable. Deliverables I need from you: • A well-documented predictive model with reproducible code and clear version control • Cleaned and transformed datasets stored back into SQL (or a recommended alternative) • An interactive Power BI report that updates seamlessly with new data • A short technical memo outlining methodology, performance metrics and next-step recommendations Strong analytical thinking, concise communication and a results-oriented mindset will set you up for success on this project. If you live in Latin America and you’re excited to turn raw financial data into actionable foresight, let’s get started.