Summary
Daniel Gil-sanchez is a Data Scientist and Engineer with over a decade of experience building reproducible, metadata-driven data ecosystems for development, public health, housing, and transport policy. At the World Bank he leads engineering for Data360—designing 150+ automated pipelines and a Python framework intended to become an internal package—while championing Scrumban, CI/CD, and rigorous data governance. He has blended academic rigor (publications and tool development at the University of Cambridge) with entrepreneurship as a co-founder of an AI/data consultancy, delivering real-time dashboards and API integrations for global decision-makers. Comfortable across R, Python, SQL and modern tooling like Kedro and Databricks, he focuses on automation and transparency so teams can move faster with cleaner, FAIR-aligned data. Unexpectedly, his background in systems-and-computing from technical high school plus an MSc in Statistics gives him an unusual combination of low-level tooling skill and high-level statistical modeling.
10 years of coding experience
3 years of employment as a software developer
Master of Science - MSc, Statistics, Master of Science - MSc, Statistics at KU Leuven
Bachelor's degree, Statistics, Bachelor's degree, Statistics at Universidad Nacional de Colombia
High School - Career and Technical Education (CTE) in Systems and Computing, High School - Career and Technical Education (CTE) in Systems and Computing at Escuela Tecnológica Instituto Técnico Central
English, Spanish