Summary
Fernanda Alba is an applied mathematician and actuarial scientist with an MSc in Computer Science and 11 years of experience leading data science teams to build scalable, production-ready analytics for finance, energy, healthcare and public-sector anti-corruption initiatives. As Area Director of the National Digital Platform she defines data strategy and interoperable systems that turn disparate public data into actionable intelligence to fight corruption in Mexico. She blends academic research—visiting scholar work at Carnegie Mellon and thesis-level NLP and deep learning projects—with hands-on product delivery as an independent consultant and former lead data scientist at major financial platforms. Fernanda is comfortable bridging technical and non-technical stakeholders, designing value propositions for healthcare technologies, and teaching intensive data science courses in Python and R. She’s currently focused on deploying data technologies at scale and has practical expertise in time-series deep learning, NLP for machine reading comprehension, and productionizing ML workflows.
11 years of coding experience
2 years of employment as a software developer
High-school, High-school at Tecnológico de Monterrey
Speaker at Data Day Mexico 2017, Deep learning to model time series data, Speaker at Data Day Mexico 2017, Deep learning to model time series data at Data Day México
Visiting Research Scholar, Visiting Research Scholar at Carnegie Mellon University
Bachelor of Applied Science (B.A.Sc.), Actuarial Sciences, Thesis with special mention, Bachelor of Applied Science (B.A.Sc.), Actuarial Sciences, Thesis with special mention at Instituto Tecnológico Autónomo de México
English, Spanish