I have a collection of structured datasets—think spreadsheets and relational tables—that I need to turn into clear, actionable insights. I’m looking for a software engineer who is comfortable applying modern AI techniques to data analysis. The job revolves around building (or fine-tuning) models that can clean, explore, and interpret these tables, then surface patterns, predictions, or recommendations that a non-technical stakeholder can understand. Here’s how I see the engagement: • You’ll review a sample of the datasets with me, clarify objectives, and map out the most suitable approach—whether that’s classic machine-learning with scikit-learn or a more advanced pipeline in PyTorch, TensorFlow, or similar. • Once the plan is agreed, you’ll implement the solution in clear, well-documented code (Python is preferred) and package any notebooks, scripts, or APIs required to run end-to-end. • Final delivery includes a concise report or dashboard summarising key findings so I can share results with the wider team. When you respond, focus on your relevant experience solving structured data problems with AI. Links to previous projects or repositories where I can see clean, well-commented code will help me quickly understand your style. I’m ready to start as soon as I find the right fit and will be available for prompt feedback throughout the build.