Remote Sensing Data Scientist at SyngentaGroup

At Syngenta, we are building the most collaborative and trusted team in agriculture to provide leading seeds innovations that enhance the prosperity of farmers worldwide. Our Digital Germplasm Development Team is seeking a motivated Remote Sensing Data Scientist who will be instrumental in revolutionizing how we leverage geospatial intelligence to accelerate breeding programs and bring superior seeds to market faster. In this role, you will support the development and deployment of computer vision solutions that extract digital traits from multi-modal plant imagery to accelerate breeding decisions and enable self-supervised learning products. Drive the strategic vision for scalable phenomics platforms that transform raw imagery from drones, ground-based sensors, and mobile devices into actionable breeding insights and AI-powered identification tools for internal and external users. As an individual contributor, you'll combine technical excellence with scientific rigor to transform raw imagery into actionable breeding intelligence. This is an opportunity to apply cutting-edge remote sensing and AI technologies to solve real-world agricultural challenges on a global scale. Accountabilities: • Design and maintain scalable, production-grade remote sensing data pipelines that ingest, process, and manage multi-source geospatial imagery supporting global breeding operations. • Develop and operate automated image preprocessing and quality-control workflows to reliably transform raw imagery into analysis-ready data. • Build and deploy machine learning and computer vision solutions that extract breeding-relevant insights from large-scale, multi-temporal imagery datasets. • Integrate multi-sensor data sources (satellite, drone, ground-based, and environmental data) into unified analytical frameworks enabling advanced spatial analysis. • Deliver time-series and geo-temporal analyses that characterize crop growth, phenology, and genotype-by-environment interactions across global trial networks. • Develop embedding-based approaches that learn rich representations from imagery for downstream prediction tasks (yield, stress, disease, trait values). • Leverage foundation models and transfer learning (vision transformers, self-supervised learning, geospatial foundation models) to create robust, generalizable solutions across crops and environments. • Partner closely with data engineering and IT teams to design and implement a unified enterprise geospatial data platform and enable efficient spatial querying at scale. • Collaborate effectively with relevant subject matter experts and data science colleagues to ensure solutions are scientifically sound, reusable, and aligned with business priorities.