Alicia García-raboso is a seasoned technical leader with 12 years of experience, currently heading CS Technical Architecture at BBVA in Madrid. She combines strong academic rigor—a PhD-level background in mathematics and physics—with hands-on engineering, having contributed bug fixes and tests to notable open-source projects including pandas and a popular Redux API middleware. Her career spans research and industry roles from CERN and top North American universities to cloud and data architecture leadership at Open Sistemas and BBVA. Alicia excels at translating complex data and systems problems into reliable, production-ready architectures and has a knack for improving code quality and robustness. Fluent in both research-driven and enterprise environments, she brings a rare mix of theoretical depth and practical delivery to large-scale financial systems. An understated strength is her continuous involvement in open-source maintenance work that boosts reliability across widely used data tooling.
11 years of coding experience
5 years of employment as a software developer
Bachelor’s Degree, Mathematics, Premio Extraordinario Fin de Carrera, Bachelor’s Degree, Mathematics, Premio Extraordinario Fin de Carrera at Universidad Autónoma de Madrid
Doctor of Philosophy (Ph.D.), Physics, Doctor of Philosophy (Ph.D.), Physics at Rutgers University-New Brunswick
Doctor of Philosophy (Ph.D.), Mathematics, Doctor of Philosophy (Ph.D.), Mathematics at University of Pennsylvania
Contributions:14 releases, 94 commits, 16 PRs in 1 year 8 months
Contributions summary:Alicia primarily worked on fixing bugs and improving the code quality of the project. They made several changes to the `src/index.js` file, correcting mistakes and refactoring the code. Furthermore, they implemented and added tests to the project code base. These changes improve the overall quality and reliability of the Redux middleware for calling an API.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Role in this project:
Back-end Developer
Contributions:6 commits, 8 PRs, 91 comments in 1 month
Contributions summary:Alicia contributed to bug fixes and enhancements within the pandas library, focusing on data manipulation and analysis functionalities. They addressed issues related to DataFrame and Series behaviors, including describe() method for boolean columns and correct calculations for `cumsum` with `axis=1`. Furthermore, the user refactored code to ensure proper file closing in parsers and implemented fixes in date-related functionality.
pythondatalabeled-datamanipulationdataframes
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Alicia García-raboso - Head Of CS Technical Architecture at BBVA