We are seeking a highly motivated geospatial data scientist with demonstrated experience applying geospatial datasets and models in agriculture to join our Science team within Digital Farming Solutions at Bayer Crop Science. You will work closely with interdisciplinary scientists, leveraging environmental and farm management data to develop geospatial data layers that provide agronomic insights to our farmer customers. 

YOUR TASKS AND RESPONSIBILITIES  

The primary responsibilities of this role are to: 

• Clean, munge, and explore diverse environmental and/or geospatial datasets 

• Analyze and judge the quality of data, and work with other scientists to resolve issues 

• Develop environmental and geospatial layers, and test these layers in predictive models 

• Design, prototype, and implement models to explore spatio-temporal dynamics and variability in agricultural fields using diverse techniques that include, but are not limited to process modeling, statistical modeling, machine learning, data mining, and optimization 

• Independently articulate project data and model limitations and advantages, defending and communicating conclusions for technical and non-technical audiences 

• Diagnose and/or predict patterns of crop performance 

• Follow software development best practices and produce code that anticipates scaling and production, assisting with code review and providing expert consultation 

WHO YOU ARE 

Bayer seeks an incumbent who possesses the following: 

Required: 

• MS in Spatial Data Science, Data Science, Remote Sensing, GIS or related field, plus 2 years of related experience in agricultural applications 

• Minimum 2 years of demonstrated experience in applying scalable geospatial data analysis and modeling solutions for agricultural applications  

• Strong analytical and quantitative problem-solving skills, with demonstrated ability to build, test, and iterate on a variety of model forms for agricultural applications 

• Fluency in programming languages and tools commonly used for geospatial workflows and machine learning including Python, GeoPandas, Shapely, Xarray, Rasterio, Spark/Sedona, scikit-learn, PyTorch, Tensorflow, etc. 

• Proven experience with software development best practices such as modularity, version control, documentation, code review, and automated test design  

• Experienced in working with cloud technologies including GCP, AWS, and Docker. 

• Demonstrated ability to work in a cross-functional environment, with strong interpersonal and communication skills, and ability to translate complex technical concepts across agricultural, scientific, and business domains 

Preferred: 

• Experience with remote sensing and/or large geospatial data 

• Spatial applications including spatial statistics, spatially explicit ML, and geospatial engineering 

• Experience designing, developing, and testing geospatial pipelines applied to machine learning model generation and deployment 

• Software engineering skills including git version control systems, SQL, workflow orchestration, API development, unit testing, and CI/CD 

• Remote Sensing tools and platforms including Google Earth Engine, Sentinel-1/2, Landsat, MODIS, and NAIP 

• Experience using multiple forms of AI such as deep learning, neural networks, or predictive analytics to drive decisions, using new algorithms efficiently and rigorously. 

Employees can expect to be paid a salary of approximately $110-160k. Additional compensation may include a bonus or incentive program (if relevant).? Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc..? This salary (or salary range) is merely an estimate and may vary based on an applicant's location, market data/ranges, an applicant's skills and prior relevant experience, certain degrees and certifications, and other relevant factors. 

This posting will be available for application until at least 8/19/25.