Data Scientist

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This project aims to build and deploy an advanced AI model that forecasts weekly and monthly case intake volumes at a law firm, while also delivering detailed attribution insights into historical trends across the previous week, previous month, and year-to-date (YTD). The model will combine structured firm data with AI-driven pattern recognition to identify the most influential drivers of case intake, enabling enhanced operational efficiency, staffing decisions, and strategic planning. Objectives Predict case intake at weekly and monthly intervals, improving resource planning and marketing timing. Generate attribution analyses that explain fluctuations in intake volume for the prior week, month, and YTD — broken down by case type, referral source, marketing channel, and geography. Leverage modern AI techniques to identify nonlinear trends, behavioral signals, and external correlations not obvious through traditional analytics. Deploy results through an interactive dashboard, providing operations and leadership with real-time insights. Key Deliverables AI Forecast Engine: A model trained on historical intake data capable of weekly/monthly prediction. Attribution Module: A sub-system that explains observed intake changes in recent time periods (prior week/month/YTD). Interactive Dashboard: User-facing interface for exploring forecasts, attribution, and historical data. Data Pipeline: Automated ingestion and transformation of structured and unstructured data. Model Explainability Framework: Tools for interpreting predictions, such as feature importance or local interpretability.

Price: $45.0

Python Machine Learning Data Science