Descripción de la oferta
About SyngentaAt Syngenta Group, we're a global community of 56,000 innovators across 90 countries, united by a 250-year legacy of agricultural excellence. As the world's most local agricultural technology partner, we create tailor-made solutions that transform farming while protecting our planet, driven by our commitment to innovation, ethics, and integrity. Through our inclusive environment and diverse perspectives, we pioneer breakthrough solutions for farmers, society, and future generations. Join our worldwide teams of agricultural pioneers in creating a more resilient and equitable food system for all. Job DescriptionAt Syngenta, we are building a collaborative team dedicated to advancing agriculture through science and innovation. Our Computational Agronomy Science team is seeking aComputational Agronomy Scientist based in Spain to develop intelligent recommendation systems and decision support models that optimize the use of Syngenta's Seed and Crop Protection products. In this role, you will bridge agronomic expertise with data science to create digital solutions that directly support farmers worldwide. Working with interdisciplinary teams including agronomists, data scientists, engineers, and product managers, you will translate complex agricultural challenges into practical, data-driven solutions that enhance crop management and protect plant health. Develop and implement agronomic models and data-driven recommendation systems for plant health protection and crop management optimization, including product selection and timing, planting decisions, fertilization, irrigation, and harvest timing. Design, build, and validate predictive models supporting our Crop Protection portfolio (fungicides, insecticides, herbicides, seed treatments, and biologicals). Analyze and integrate diverse agricultural datasets—including R&D field trials, on-farm demonstrations, weather data, soil characteristics, pest monitoring, and agronomic scouting records—to generate actionable insights. Design and analyze field experiments, including statistical validation, data quality assessment, and interpretation of multi-location trial results to support product development and agronomic recommendations. Create data processing pipelines using Python to clean, transform, and analyze agricultural datasets for model development and validation. Develop presentations and visualizations to communicate model outcomes and analytical findings to both technical and non-technical stakeholders. QualificationsRequired Qualifications: MS degree in Agronomy or related agricultural sciences (PhD preferred). 5+ years of professional experience building predictive models or in roles involving agricultural data analysis. Proficient English language skills (written and verbal) for international team collaboration. Strong proficiency in Python programming with experience in data analysis libraries (e.G. Pandas, NumPy, Scikit-learn) and visualization tools (e.G. Matplotlib, Seaborn, Plotly) Understanding of applied statistics and experience applying AI/ML techniques, including supervised learning, time series forecasting, clustering, segmentation, and Bayesian inference. Comprehensive knowledge of crop production systems, including agronomic management practices, growth stages, yield-limiting factors, and crop protection practices. Strong understanding of pest management (insects, diseases, weeds), including pest lifecycles, economic thresholds, ROI concepts, and integrated pest management (IPM) principles. Practical knowledge of field-level agronomic research protocols, including plot management, data recording, and quality control in experimental settings. Demonstrated ability to leverage generative AI tools (ChatGPT, Claude, GitHub Copilot) to enhance productivity and accelerate problem-solving. Professional Capabilities: Strong analytical and problem-solving skills with ability to bridge agronomic knowledge and computational methodologies. Ability to work independently and collaboratively in a fast-paced, innovation-driven environment while focusing on key priorities. Ability to translate technical solutions into practical business outcomes with clear value for customers. Desired Qualifications: Professional experience in agricultural research, crop consulting, digital agronomy, or related agricultural data analysis roles. Experience with machine learning frameworks such as CatBoost, XGBoost, LightGBM, PyMC, and StatsModels. Experience with crop simulation models (DSSAT or APSIM). Experience working with geospatial datasets. Experience in cloud environments such as Amazon SageMaker. Experience collaborating with data engineers and ML engineers to develop, optimize, and deploy model solutions. Familiarity with Agile software development principles and tools. Knowledge of agrometeorology and its application to pest forecasting. Familiarity with field monitoring using IoT devices, sensors, soil and plant sampling, and scouting techniques. Additional InformationWhat We Offer: An environment where every voice matters, professional growth is encouraged, and work-life balance is prioritized.Comprehensive benefits package that starts from day one, including health and wellness coverage tailored to your location.Generous annual leave entitlement, learning and development opportunities, wellness programs, employee assistance programs, and additional benefits in accordance with local practices. 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. #J-18808-Ljbffr
