- MS in Statistics, Data Science, Math, Physics, Computer Science, Engineering, Soil Science, Geography, or other highly quantitative discipline + 2 years' experience conducting geospatial data analysis and modeling with Python, or PhD in one of the aforementioned disciplines
- Demonstrated ability to apply spatial statistical methods and/or data science to real world problems.
- Strong analytical and quantitative problem solving skills, with demonstrated ability to build, test, and iterate on a variety of model forms
- Demonstrated ability to work in a cross-functional environment, with strong interpersonal and communication skills, and ability to translate complex technical concepts across scientific and business domains
- Experienced in working with cloud technologies or platforms such as AWS, Azure, Google Cloud etc.
Preferred Qualifications:
- Experience with remote sensing, agricultural, and/or large geospatial data
- Experience designing, developing, and testing geospatial pipelines applied to machine learning model generation and deployment.
- Experience using version control systems, e.g. git, github etc.
- Certifications in data analysis / sciences using Python.
- Experience using multiple forms of AI such as machine learning, deep learning, neural networks, or predictive analytics to drive decisions, using new algorithms efficiently and rigorously.