Meigarom Lopes is a Lead Data Scientist with 9+ years building production ML systems across startups and scale-ups, known for driving measurable business impact like a 68% CAC reduction and >80% forecasting accuracy. He combines hands-on MLOps (Airflow, AWS, Databricks) with applied expertise in reinforcement learning for budget allocation, Bayesian A/B testing and recommendation systems, delivering end-to-end solutions from model training to scalable APIs. His work spans computer vision, time series, and hybrid models (XGBoost+LSTM), and he has repeatedly turned models into production features that improved CTR, retention and inventory efficiency. Comfortable leading teams and building data platforms, he’s also experienced migrating legacy infra to cloud-scale architectures and automating retraining pipelines. A former exchange student at UMKC with a background in electrical engineering, he brings a systems-thinking approach that blends experimentation rigor with product-facing prioritization. Now open to remote international roles, he’s focused on scaling business-oriented ML in collaborative environments.
9 years of coding experience
10 years of employment as a software developer
Electrical and Mechanical Technician Electrical and, Electrical and Mechanical Technician Electrical and at Etec Getúlio Vargas
Electrical Engineering Machine Learning Data Mining and Pattern Recognition in Biometric, Electrical Engineering Machine Learning Data Mining and Pattern Recognition in Biometric at University of Missouri-Kansas City
Electrical Engineering System of Energy Control and Automation Energy System and Automation, Electrical Engineering System of Energy Control and Automation Energy System and Automation at USP - Universidade de São Paulo
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