Igor Fernandes is an applied scientist blending a strong statistical foundation (M.S. in Statistics) with 7+ years building and shipping ML/AI solutions across agriculture, e-commerce, and environmental analytics. He designs end-to-end workflows—from feature engineering and rigorous statistical modeling to Dockerized deployment and Azure-based MLOps—bringing production reliability to research-grade models. Recent work includes RAG-based GenAI systems and time-series risk forecasting that delivered measurable operational savings and improved decision-making. A competitive data scientist, he has won international agritech challenges and open-sourced reproducible competition solutions on GitHub, demonstrating both academic rigor and pragmatic engineering. He’s comfortable translating complex models into business value for non-technical stakeholders and mentoring junior teammates on ML engineering best practices. Notably, his research-grade pipelines have scaled from HPC clusters for multi-omics to cloud-native deployments, showing versatility across infrastructure and scientific domains.
7 years of coding experience
2 years of employment as a software developer
Bachelor's degree, Statistics, Bachelor's degree, Statistics at Universidade Federal de Goiás
Master's degree, Statistics and Analytics, Master's degree, Statistics and Analytics at University of Arkansas
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Igor Fernandes - Applied Scientist at Heritable Agriculture