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michael santos

@michaelsantos1

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Senior data analyst specializing in fraud detection, revenue protection, and analytics.

Brazil

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What I'm looking for

I seek a role applying fraud detection and revenue-protection analytics, with strong collaboration with operations and executives, emphasis on scalable ML pipelines and cloud-native ETL that deliver measurable financial impact.

I am a Senior Data Analyst with 4+ years delivering measurable revenue protection outcomes for regulated water utilities, combining Python and SQL engineering with business strategy to drive C-suite decisions.

I have led development of production fraud-detection systems processing 2M+ customer records and 500K+ monthly transactions, achieving 35–60% efficiency gains and recovering millions in lost billing revenue.

My technical strengths include end-to-end machine learning pipelines, cloud-native ETL architectures (AWS, GCP, Azure, Databricks), and scalable SQL optimizations that cut processing time by up to 60% while improving data quality.

I design executive-grade Power BI dashboards and automated workflows that enable real-time monitoring, prioritized investigations, and operations improvements—translating analytics into measurable cost savings and operational impact.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Analyst

Current

Sabesp

Dec 2025 - Present (3 months)

Engineered risk-scoring algorithms analyzing 2M+ customer records to prioritize 50K+ fraud investigations, improving field team efficiency by 35% and accelerating recovery of millions in annual billing. Architected automated ETL from Oracle to Azure Databricks, improving data quality 40% and cutting SQL pipeline processing time by 60%.

Python SQL Databricks Random Forest ETL Data Quality Fraud Detection

Data Analyst

BRK Ambiental

Feb 2024 - Nov 2025 (1 year 9 months)

Built analytics platform processing 500K+ monthly records with Python and BigQuery, reducing manual reporting time by 60% and enabling real-time oversight. Deployed Random Forest models (85% accuracy) and time-series forecasts that cut treatment material costs by 15%.

Python BigQuery Power BI Random Forest Time Series ETL

Research Assistant

Unicamp

Mar 2021 - Feb 2022 (11 months)

Analyzed wastewater treatment performance using Python and statistics to identify operational inefficiencies and developed standardized analytical workflows and Power BI dashboards adopted across research groups.

Python Statistical Analysis Power BI Data Visualization Data Cleaning