Data Scientist, Gro Intelligence

• 3+ years of experience creating real world machine learning systems like model training, validation, feature engineering, hyperparameter tuning etc
• Ability to work with Python (including packages such as pandas, numpy, scikit-learn, etc.), Shell, Git, Docker
• Expertise with large-scale geospatial and temporal data sets and the application of scalable machine learning on the subject
• Familiarity with cloud computing tools such as AWS, Azure, GCP
• Ability to work collaboratively with team members across functional roles and strong communication and leadership skills
• Passion for solving high impact problems with data driven tools
• Interested in new skills and adapting to new technologies
• Ability to work with geographically distributed and culturally diverse team
• Ability to generalize and abstract

Desirable

• Develop original spatial-temporal models to predict agricultural and environmental performance and conditions using agricultural, weather and climate data
• Develop systems and techniques for very large scale computations
• Work with scientists, analysts and engineers to integrate local and sector specific factors and work closely with specialists in the field to further internal product development and external collaborations
• Do original research on machine learning models and algorithms
• Understand and contribute to the advancement of the state of the art in academic and industry research