Senior Agronomical Engineer - South America Crop Forecasting

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology- and data-driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset, which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high-quality returns for our investors.

We are looking for a Senior Agronomical Engineer (South America Crop Forecasting) to join our Commodities team in Geneva. The Senior Agronomical Engineer is responsible for developing and validating proprietary production forecasts for major South American crops, including soybeans, corn, and wheat. This expert-level role combines deep regional agronomic knowledge with advanced quantitative data science to support high-value agricultural commodity research and supply modelling.

• Analyse crop development across Brazil, Argentina, Paraguay, and Uruguay; assessing planting progress, phenology, yield formation, pest and disease pressures, and climate risks; and translating field observations into structured yield and production forecasts using statistical and machine learning models.
• Regular field visits to collect ground-truth data, evaluate crop conditions and management practices, maintain regional agronomic networks, and validate satellite-derived indices against observed conditions.
• Integrate multi-resolution satellite and drone imagery with geospatial datasets, applying advanced statistical methods to refine proprietary forecasting models and address structural biases related to crop calendars and double-cropping systems.

• Degrees in Agronomical Engineering and Agronomy (or related field), a postgraduate qualification in quantitative data science or analytics.
• Demonstrated professional experience in South American soybean, corn, and/or wheat production systems.
• Experience working with remote sensing and geospatial agricultural datasets.
• Advanced proficiency in Python and applied statistical modelling.
• Ability to independently integrate field agronomy with quantitative forecasting frameworks.