Almudena Olivé
Principal Data Scientist
Greater Madrid Metropolitan Area Spain
Summary
🤩
Rockstar🎓
Top SchoolAlmudena Olivé is a Principal Data Scientist with 12 years of experience building production-ready ML and recommendation systems, currently leading data science efforts at Elastic in the Greater Madrid area. She has a strong blend of industry and startup experience—from co-founding a 3D-printing company to senior roles at TENDAM and McKinsey—coupled with teaching Python at a university bootcamp. Her open-source work includes integrating Wikidata and SPARQL-backed knowledge into recommender systems, showing a knack for combining external knowledge graphs with robust error-handling for reliable pipelines. Educated across engineering, computer science, and economics programs (including UC Berkeley exchanges), she brings multidisciplinary thinking to product-focused ML problems. Colleagues describe her as a pragmatic engineer who balances research-grade methods with scalable, production-minded implementations.
12 years of coding experience
10 years of employment as a software developer
Exchange Student - Bachelor of Engineering (B.Eng.) Mechanical Engineering Industrial Engineering CS, Exchange Student - Bachelor of Engineering (B.Eng.) Mechanical Engineering Industrial Engineering CS at University of California, BerkeleyBSc Economics and Politics (academic direction from the London School of Economics and Politics), BSc Economics and Politics (academic direction from the London School of Economics and Politics) at University of LondonCharles III University of Madrid (Universidad Carlos III de Madrid)International BaccalaureateMaster's degree Computer Science, Master's degree Computer Science at Universidad Nacional de Educación a Distancia - U.N.E.D.
Spanish, English, French
Github Skills (12)
json-parser10
json10
recommendation-system10
sparql10
parsing10
python10
api-design9
data-science9
ai9
machine-learning9
api9
apim9
Programming languages (3)
Github contributions (5)
recommenders-team/recommenders
May 2019 - Feb 2020
Best Practices on Recommendation Systems
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:129 commits, 7 PRs, 53 pushes in 9 months
Contributions summary:Almudena's contributions center on implementing and refining Wikidata-related functionalities within the recommender system project. They developed a Python module containing functions to query Wikidata, retrieve entity descriptions, and establish links between entities. The code additions demonstrate the user's ability to interact with external APIs (Wikipedia/Wikidata), parse JSON data, and leverage SPARQL queries to integrate knowledge graphs with the recommendation system. The improvements involved refining the code, in particular error handling, to ensure reliable data extraction from the Wikidata API.
recommendation-systemmachine-learningrecommenderrankingdeep-learning
almudenasanz/kibana
Jun 2023 - Feb 2025
Your window into the Elastic Stack
Contributions:106 pushes, 5 branches in 1 year 8 months
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