Juan José Leitón Montero, Developer in San José, Costa Rica
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Juan José Leitón Montero

Verified Expert  in Engineering

Data Scientist and Developer

San José, Costa Rica
Toptal Member Since
August 18, 2022

Juan is a data scientist and developer with ten years of industry experience and a master's degree in statistics. During his career, Juan has been focusing on renewable energy, climate projections, and creating academic courses. Juan believes in continuous education and is working toward a master's degree in applied mathematics at the University of Costa Rica, where he teaches statistics and engineering to graduate students.



Preferred Environment

R, Python 3

The most amazing...

...thing I've worked on is a study on quantifying an energy system's uncertainty primarily based on renewables.

Work Experience


2018 - PRESENT
University of Costa Rica
  • Devised a computational statistics course for a master's degree program in statistics.
  • Expanded the master's degree statistics program by developing a spatial statistics course.
  • Collaborate on creating the master's degree program in data science as a committee member.
Technologies: Computational Statistics, Spatial Statistics, R Programming

Data Analyst

2012 - 2022
Costa Rican Electricity Institute
  • Developed a methodology for quantifying uncertainty in a renewable energies portfolio using R.
  • Estimated the sediment production yield for Costa Rica's basins using regression models.
  • Served as a technical advisor for the government of Costa Rica during the oral proceedings before the International Court of Justice in The Hague, Netherlands.
Technologies: R, SQL

Junior Engineer

2012 - 2012
STC Grupo Empresarial
  • Analyzed potential sites for hydropower development using GIS and Python.
  • Conducted extreme value theory analyses of hydrometeorological variables for design purposes.
  • Used R to analyze hydrological time series for water resource management.
Technologies: CAD, GIS

Statistical Analysis of Multi-model Climate Projections for Europe

Used Bayesian hierarchical modeling to analyze the seasonal temperature and precipitation projections of regions covered by the PRUDENCE projects, focusing on CH2018 climate scenarios and the RCP8.5 warming scenario. This model implementation expands the work by Kerkhoff, Tay, and Künsch by evaluating temperature and precipitation variables for every combination of regions and seasons.

I used posterior distributions to estimate parameters associated with bias and assumption coefficients, climatological means, interannual variables, and additive bias, in addition to calculating climate change estimates for five different time horizons. Also, I found a generalized variation temperature pattern of all analyzed region and season combinations and identified season- and region-dependent patterns for precipitation.

By comparing bias associated with the regional climate and general circulation model chains and their drivers, I evaluated the additive bias reduction from dynamic scaling. Finally, I assessed the results accounting for a potential 20% component reduction and classified the combinations of regions, seasons, and chains based on this value.

Quantifying Uncertainty for the Renewable Energy Transition

Modeled hydroclimatic time series using autoregressive models, Gaussian processes, and wavelets to quantify the national electricity system's energy portfolio uncertainty.

I utilized five different modeling approaches to reproduce the stochastic characteristics of a set of hydroclimatic time series. The modeled time series were evaluated based on the parent distribution, distribution of extremes, and multivariate and temporal dependencies.
2013 - 2022

Coursework in Applied Mathematics

University of Costa Rica - San Pedro, Costa Rica

2016 - 2018

Master's Degree in Statistics

ETH Zürich - Zürich, Switzerland

2005 - 2012

Licentiate Degree in Civil Engineering

University of Costa Rica - San Pedro, Costa Rica




R, Python 3, SQL



Industry Expertise

Project Management


Visualization, Statistical Modeling, Regression, Spatial Statistics, R Programming, Value Analysis, Quantitative Risk Analysis, Multivariate Statistical Modeling, Dimensionality Reduction, Causal Inference, Stochastic Modeling, Computational Statistics, Probability Theory, Bayesian Inference & Modeling, Time Series Analysis, Bayesian Statistics

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