ZHOYA MARIE GINEZ

Develop and refine AI/ML solutions for intelligent document processing and predictive analytics. Build predictive models to score existing policy holders, improving lead prioritization and supporting data-driven business decisions. Design and continuously improve prompt engineering workflows using Cytora for document data extraction, leveraging a custom evaluation framework in Databricks to measure and improve extraction quality. Use Python- and Databricks-based pipelines to enhance operational efficiency.

Develop data products, predictive models, and reporting solutions to support marketing operations and business innovation initiatives. Built a forecasting model using NeuralProphet to optimize game resource allocation and improve operational planning and staffing efficiency. Designed, maintained, and enhanced Hive-based data marts to ensure reliable, relevant operational data availability. Monitored, evaluated, and retrained production predictive models to maintain accuracy over time. Produced reports and analytical solutions to support data-driven decision-making across marketing and operations teams.

Delivered advanced analytics and data science solutions across business units in multiple markets to enable technology and analytics-driven decision-making. Fine-tuned a pre-trained NLP model to automate information extraction across different file formats and structures, reducing manual effort by ~90%. Developed customer segmentation models using clustering techniques to support personalized marketing campaigns, contributing to an estimated 1.5% increase in conversion rates. Conducted post-campaign analyses and proposed improvements for future campaign optimization. Proposed and initiated a lead scoring model project to improve lead prioritization and marketing effectiveness, collaborating with regional stakeholders to translate business opportunities into scalable analytics solutions.