📍 New York, NY 10001
Data Analyst fluent in English, Spanish, and Italian, with a robust background in economics. Based in New York City, I specialize in Python and Rust for data analysis. My toolkit includes extensive use of Python for data-driven insights and Rust for high-performance networking, with a keen interest in Cloudflare's innovative use of Rust to enhance web infrastructure.
- Python Libraries:
- Polars & Numpy: Utilize for data manipulation and numerical analysis to clean and prepare large datasets.
- Matplotlib & Statsmodels: Employ for data visualization and statistical modeling to uncover trends and patterns.
- Machine Learning with Python:
- Scikit-learn: Implement machine learning algorithms for predictive modeling.
- PyTorch: Use for building and training advanced neural network models.
- Data Management:
- SQL: Manage databases efficiently.
- Excel: Utilize for additional data processing and visualization.
- Rust Libraries:
- Axum: Leverage for creating high-performance web applications and APIs with ease.
- Tokio: Use for asynchronous programming to handle multithreaded network operations efficiently.
- WASM (WebAssembly): Explore for compiling Rust to WASM, enabling high-performance, low-level operations in web environments.
- Rust in Data and ML:
- Polars: Use for data processing in Rust, similar to how it's used in Python, for fast data manipulation.
- Burn: Experiment with this Rust library for machine learning, aiming to bring Python's ML capabilities into Rust's performance ecosystem.
- TypeScript:
- Employ for type-safe frontend development, enhancing scalability and maintainability of web applications.
- Integrate with React or Qwik for dynamic, data-driven UIs.
Bachelor of Science in Economics | UNAM (2018 - 2023)
- Applied advanced statistical techniques and Python programming to model and analyze economic data.