I'm driven by curiosity and the thrill of solving complex challenges. My journey began with an early exposure to programming in law school, which later led me to specialize in applied mathematics and public administration. From uncovering hidden insights in massive datasets to designing machine learning models for predictive analysis, I aim to make data meaningful, actionable, and accessible to all.

• Machine Learning & AI

  • Developing predictive models and deploying solutions with Python, PyTorch, and scikit-learn.
  • Leveraging Natural Language Processing (NLP) with BERT, Transformers, and custom algorithms to process complex texts and drive insights.

• Data Engineering & Automation

  • Extensive experience with data pipelines and ETL processes, ensuring accuracy and scalability across various environments.
  • Skilled in using AWS (Athena, Redshift), Google BigQuery, and Azure for efficient cloud-based data management.

• Data Visualization & Communication

  • Designing impactful, high-level dashboards in Power BI, Tableau, and Quarto.
  • Creating clear, insightful reports for stakeholders and the public, simplifying complex data insights for broader audiences.

• Data Wrangling & Academic Analysis

  • Skilled in cleaning, structuring, and wrangling large and often messy datasets from diverse sources, including databases from the public sector and private repositories.
  • Experienced in conducting rigorous analyses within academic settings and publishing findings in peer-reviewed journals and relevant industry publications.

• Languages & Libraries: Python, R, SQL, scikit-learn, PyTorch, Transformers
• Visualization: Power BI, Tableau, Quarto, ggplot, Matplotlib
• Data Handling: AWS (Athena, Redshift), Google BigQuery, MongoDB, PostgreSQL
• Modeling Techniques: Clustering (K-means++, DBSCAN), Predictive Behavior Modeling, Regression Analysis

• Baltimore Building Risk Assessment
  Co-developed a neural network model (91% accuracy) for identifying at-risk buildings using aerial images, in collaboration with Carnegie Mellon University and Baltimore's Housing Department. The project earned an innovation award and significantly enhanced public safety initiatives.

• Wage Inequality & Public Education Transparency
  Analyzed socioeconomic data of 800,000+ public school students in São Paulo, uncovering significant disparities and influencing local policies.

• Public Procurement Analysis & Fraud Detection
  Developed algorithms to automate data collection and analysis of government procurement, uncovering instances of misappropriation and aiding legal investigations.

• LinkedIn: /in/jonasbarros/
• Website: https://jncoe.github.io/

Thanks for stopping by! Feel free to explore my repos and reach out if you’d like to collaborate on exciting data projects or just connect!