Full-Stack Developer with ML/Data Engineering background to build an AI-powered economic modeling and simulation system
Budget: $15 – $25 USD
I am assembling a full-stack solution that ingests live financial databases, open-source macroeconomic feeds, and user-submitted figures, then turns all of it into forward-looking insights. The core of the product is an AI layer that delivers predictive analytics and runs configurable scenario simulations; the results need to surface instantly inside an intuitive web dashboard where users create and save their own reports and visual layouts.
What I need from you is an end-to-end build:
• Data layer – connectors that automatically pull structured and unstructured data from the three source categories above, plus a storage design that can scale as historical records grow.
• ML & simulation engine – time-series forecasting models, Monte Carlo or agent-based simulation methods (whichever you recommend for accuracy and speed), and a clean API that the front end can query for fresh results.
• Interactive UI – a modern single-page app with drag-and-drop widgets, charting/heat-map components, and export options so users can craft their own dashboards and download reports.
Security, unit tests, and deployment scripts (Docker or similar) should accompany the code, and clear developer documentation is part of the final hand-off. If you have previous work blending data engineering, machine learning, and rich visualisation, that will help me understand how quickly we can reach production.
What I need from you is an end-to-end build:
• Data layer – connectors that automatically pull structured and unstructured data from the three source categories above, plus a storage design that can scale as historical records grow.
• ML & simulation engine – time-series forecasting models, Monte Carlo or agent-based simulation methods (whichever you recommend for accuracy and speed), and a clean API that the front end can query for fresh results.
• Interactive UI – a modern single-page app with drag-and-drop widgets, charting/heat-map components, and export options so users can craft their own dashboards and download reports.
Security, unit tests, and deployment scripts (Docker or similar) should accompany the code, and clear developer documentation is part of the final hand-off. If you have previous work blending data engineering, machine learning, and rich visualisation, that will help me understand how quickly we can reach production.