Data Scientist Geospatial Analytics

Bunge•Chesterfield, MO

2d•Onsite

About The Position

The Data Scientist, Geospatial Analytics will be a core contributor to Bunge’s Global Economic Analysis team, applying data science, satellite imagery, advanced statistical modeling, and emerging generative AI techniques to generate valuable insights across global agricultural markets. This role will lead the development of scalable, satellite-based analytics that support global crop forecasting, supply chain intelligence, and commodity market analysis. The position focuses on integrating public Earth observation data with proprietary datasets and transforming them into actionable signals that enhance our understanding of crop conditions, acreage, yield potential, and global supply-demand dynamics.

Requirements

Master’s degree or higher in Remote Sensing, Statistics, Computer Science, or a closely related quantitative field
Minimum of 5 years of professional experience in remote sensing, geospatial analytics, or agricultural data science
Advanced proficiency in Python (e.g., pandas, NumPy, scikit learn, statsmodels, TensorFlow, GeoPandas, rasterio)
Strong SQL skills for data extraction, manipulation, and analysis across large datasets
Solid understanding of geospatial data systems, projections, and large scale processing workflows
Demonstrated ability to translate complex data and models into practical agricultural, commercial, or market insights
Excellent communication and presentation skills, with the ability to explain complex analytical concepts clearly and concisely
Highly detail oriented, proactive, and self motivated, with the ability to work both independently and collaboratively in a fast paced, global environment

Nice To Haves

Background in agriculture, crop modeling, or commodity research
Experience working with radar data and vegetation indices
Exposure to yield modeling, acreage estimation, or crop classification workflows
Knowledge of agricultural commodity markets (e.g., grains, oilseeds, biofuels) and agronomic concepts

Responsibilities

Design, build and scale satellite-based analytics pipelines for real-time crop monitoring at globe scale
Analyze and integrate multi source datasets, including satellite imagery (Sentinel 1/2, Landsat, MODIS), weather and soil data, agricultural statistics, field level observations, and proprietary datasets
Develop geospatial indicators such as NDVI anomalies, crop classifications, and yield signals to support trading and commercial decisions
Leverage cloud infrastructure (e.g., Google Cloud Platform, AWS) for large scale geospatial data processing
Utilize platforms and tools including Google Earth Engine, BigQuery, and Python based analytics pipelines
Apply AI and machine learning techniques to imagery and time series data (e.g., classification, segmentation, feature extraction, and temporal modeling)
Collaborate with economists, market analysts, data engineers, and business leaders to integrate geospatial insights into market views and fundamental analysis
Monitor, evaluate, and continuously refine deployed models and analytics to ensure sustained accuracy and measurable business impact
Clearly communicate complex analytical findings, model insights, and strategic recommendations to diverse audiences, including senior leadership and traders, to support informed decision making and global risk management