Data scientist with a Mathematics background, focused on taking models all the way: from raw data and pipelines to classical ML, explainability and deployment in the cloud.
Currently at WiseDB working across data and AI projects, and expanding into AI Engineering: LLM-based systems, RAG and agentic workflows.
Data Science intern at WiseDB, a Brazilian cloud and data consultancy. I move across data and AI projects, including machine learning for people analytics: data pipelines, classical ML with explainability (SHAP) and AutoML on Oracle Cloud.
My main front right now: building agent capabilities and AI-assisted workflows, and deepening into LLM-based systems, RAG and agent memory, with the same production mindset.
End-to-end projects that show how I take data from raw sources to models and running systems.
User loyalty prediction on real transactional data: feature engineering, temporal validation, batch scoring and an inference API.
Formula 1 data pipeline with layered lakehouse design, feature store construction and an end-to-end ML workflow.
Offline-first retail support system for contingency sales operations and local reconciliation, used in real store scenarios.
End-to-end technical data case: cleaning, PostgreSQL modeling, forecasting, recommendation and Airflow orchestration.
My work is centered on connecting data, business context and implementation. I care about the full path: understanding the problem, modeling it honestly, validating it properly and shipping something that runs.
I prefer clear and defensible technical communication over inflated claims.
Open to opportunities in Data Science, Analytics and AI Engineering. If my work fits your team, I’d be glad to talk.