Bernardo Lares
Data Scientist
Madrid, Community of Madrid, Spain
Summary
🤩
Rockstar🎓
Top SchoolBernardo Lares is a Data Scientist based in Madrid with nine years of experience applying ML, BI, and data visualization to product-focused problems. He blends hands-on modeling and MLOps—demonstrated by contributions to Meta’s open-source Robyn Marketing Mix Modeling package—with automation and deployment work that reflects a practical CI/CD mindset. Comfortable in R and integrating Python tooling via reticulate, he bridges analytics and engineering to drive scalable, production-ready solutions. Bernardo’s background in product ownership and optimization means he not only builds models but shapes how they inform decision-making, and his work on a high-profile MMM project shows a commitment to democratizing advanced analytics.
9 years of coding experience
London School of Economics and Political Science
Spanish, English, Portuguese
Stackoverflow
473reputation
32kreached
8answers
25questions
Github Skills (15)
machine-learning10
r10
econometrics9
mlops9
develop8
automations7
automation7
rstudio6
datatables6
units-of-measurement6
shiny6
ggplot6
github6
travis-ci6
dt6
Programming languages (12)
Github contributions (5)
facebookexperimental/Robyn
Aug 2021 - Jan 2023
Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:15 releases, 25 reviews, 513 commits in 1 year 5 months
Contributions summary:Bernardo focused on modifying and deploying the experimental Marketing Mix Modeling (MMM) package. The contributions included adjusting .gitignore files for team collaboration. They also contributed to the deployment process by releasing a new version, showcasing involvement in CI/CD or similar build processes. The user's work involved changes in R/Robyn.Rproj and other code files, and the use of Reticulate functions related to the nevergrad Python library and additional import packages for the project, indicating the integration of machine learning components.
gradient-based-optimisationeffectivityridge-regressionadvertisersmarketing-mix-modelling
laresbernardo/lareshiny
Aug 2019 - Jun 2021
Contributions:1 release, 20 commits, 37 pushes in 1 year 10 months
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