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Structured, processed, and integrated large heterogeneous datasets focused on public safety and governmental intelligence. Automated data pipelines using Python and SQL to reduce operational inconsistencies and improve data reliability. Built analytical dashboards and intelligence reports to map patterns, operational trends, and risk scenarios.

Retail intelligence: analyzed dynamic pricing and cross-sell opportunities using exploratory data analysis in SQL, Python, and Excel, identifying incremental revenue potential estimated at $118M BRL. Mobility & risk analytics: engineered predictive models correlating rainfall with traffic incident records in RM BH using Python and LLMs (Gemini) to identify high-risk road segments and time windows. Indicator management & tracking: analyzed consumer buying behavior and habits to deliver predictive recommendations aimed at improving retention and basket value.

Collected, standardized, and structured functional data and biomechanical markers for high-performance teams. Formulated data-driven preventive plans based on individual workload metrics and evidence-based insights.