Data Science & Geospatial Consultant
I help organizations apply statistics and machine learning to climate, health, and geospatial data, turning it into models and tools that support real-life evidence-based decisions.
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Spatial models and risk indices from satellite, climate, hazard and vulnerability data
Combining epidemiological and environmental data to track outbreaks and inform policy.
Bayesian inference and reproducible pipelines (R, Python, Shiny/Dash) that turn analysis into dashboards people actually use.
Worked with
I bring together climate science, public health, statistics, and machine learning to build tools that hold up in the real world, across four connected areas of practice:
Climate & environmental risk modeling
Hazard modeling and multi-hazard risk aggregation to support climate resilience planning.
Health & environmental data integration
Combining epidemiological, environmental, and geospatial data to support public health decisions.
Geospatial analysis & spatial ML
GIS analysis, remote sensing, and spatial statistics and machine learning.
Decision-support tools for public policy
Bayesian statistics and inference to quantify uncertainty and support robust decisions, delivered through reproducible analyses and interactive dashboards (Shiny, Dash).
2026 · Project
Designed a global methodology to quantify school-level exposure to climate hazards using geospatial data and ML-based risk aggregation.
2025 · Project
Interactive map of Argentine election results since 2011 with dynamic drill-down to the electoral precinct level; public demo for Entre Ríos province.
2023 · Publication
Journal of Medical Entomology
2023 · Publication
medRxiv
2022 · Project
Built a Shiny dashboard tracking confirmed, probable, and fatal COVID-19 cases across Argentina for the education sector, with derived indicators like positivity rate, case-fatality ratio, and incidence.
2022 · Project
Spatial clustering of satellite heat-point detections in the Paraná Delta to build early-warning indicators for wildfire risk, developed for Institute Malbrán.
2022 · Publication
medRxiv
2021 · Project
Built interactive Shiny and Dash dashboards to track and communicate COVID-19 epidemiological data for the sedcovid project at CONICET/UBA.
2021 · Project
Open-source R package for extracting and structuring data from Google's 'Popular Times' feature into ready-to-use dataframes.
Matías Poullain, MSc
I'm a biologist turned data scientist based in Buenos Aires, Argentina. After a biology degree focused on ecology and statistics and a Master's in Data Mining & Knowledge Discovery, I've spent the last several years building statistical and machine learning models, and geospatial analysis solutions for organizations spanning public health, climate policy, international development, and the agricultural industry. I also teach Bayesian Inference in a Master's program at the Universidad de Buenos Aires (UBA). I work fluently across Spanish, French, and English.
Working on a climate, health, or geospatial data problem? Reach out, or find me on LinkedIn.