Brazil onlyWe're looking for a data scientist who thinks in geographic dimensions to uncover patterns that protect merchants, detect fraud, and unlock insights hiding in the intersection of place and payments.
The Geospatial Team
- We work with geospatial data from internal and external sources to create ML features and drive business insights
- We research and experiment with new algorithms that are more computationally efficient or can leverage spatial data better
- We do extensive data visualization with maps to uncover spatial insights and patterns
- We value exploration before exploitation—curiosity comes first in spatial analysis
- We believe location is more than coordinates—it's behavioral context that drives better decisions
What the Job Entails
- Spatial Intelligence: Apply data science techniques to spatial datasets and location-based problems to uncover financial behavior patterns
- Geospatial Model Development: Design and run experiments on different forms of aggregating spatial data and phenomena, then train and validate geospatial models for business applications
- Feature Engineering & Data Enrichment: Create valuable features for ML models by extracting spatial intelligence from geospatial data sources, perform ETL processes, and deliver enriched data to our internal feature market
- Data Pipeline Management: Build and maintain geospatial data pipelines and analytical workflows using modern data science tools
- Spatial Data Discovery & Integration: Continuously explore and evaluate diverse spatial data sources, research new datasets, and develop creative approaches to "spatialize" existing databases by adding geographic dimensions
- Spatial Analysis: Analyze spatial data patterns and develop spatial models and algorithms to solve business problems
- Map-Based Data Visualization: Create compelling data visualizations with maps to discover insights and communicate spatial findings to stakeholders
What you'll need
- Initiative to learn, investigate, experiment with spatial data and geographic problems
- Strong statistical and machine learning background, with experience applying these methods to spatial data
- Strong experience with map-based data visualization using tools like Plotly, Kepler.gl, or similar mapping libraries
- Experience working with spatial data formats (shapefiles, GeoJSON, etc.), coordinate systems, and geospatial dataframes using geopandas (Python) OR spatial R packages (sf, sp, etc.)
- Python proficiency (CloudWalk standard) or strong R skills with willingness to adapt to Python workflows
- Strong SQL experience for spatial data queries and analysis
- Ability to communicate and debate in English and Portuguese
Nice to Have
- Background in Geographic Information Systems (GIS) tools and concepts
- Experience with spatial statistics/econometrics and geospatial machine learning methods (e.g. clustering methods like DBSCAN, K-means, hierarchical clustering, or regression methods like GWR, MGWR, SAR)
- Experience with external data sources (IBGE research, OpenStreetMap, government open datasets)
- Experience with Google Cloud Platform
- Experience with MLOps and deploying geospatial models to production environments
Recruiting process outline:
- Online assessment: An online test to evaluate your theoretical skills and logical reasoning
- Technical interview and case presentation
- Cultural interview
If you are not willing to take an online quiz and work on a test case, do not apply.
