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A leading agricultural technology company is looking for a Geospatial Data Scientist to transform agricultural and earth observation data into insights. The candidate will use advanced geospatial analytics and machine learning to tackle critical challenges in agriculture, focusing on innovation to improve food security. The role demands a strong background in geospatial analysis, Python programming, and collaboration with various teams.
The Geospatial Data Scientist will leverage advanced geospatial analytics, machine learning, and remote sensing expertise to transform complex agricultural and earth observation data into actionable insights that drive innovation in Syngenta's Computational Agronomy Department. This role will develop cutting‑edge models and algorithms that extract meaningful patterns from diverse spatial datasets, enabling data‑informed agricultural decision‑making that supports Syngenta's mission to improve global food security and sustainable farming practices.
Working within cross‑functional teams, the Geospatial Data Scientist will bridge technical expertise with agricultural knowledge to create scalable solutions that address critical challenges in modern agriculture through the power of spatial data science.
Location: Remote working is possible within Spain.
Portfolio submission: Please provide examples of relevant geospatial data science projects.
Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion, or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status.
Remote Work: Yes
Employment Type: Full‑time
* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.