Remote, Remote
July 17, 2022
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Description of work:
- Build machine learning models with agricultural data for R&D and production use cases. Wrangle flat, nested, and spatial data and perform associated QC procedures. Leverage classical statistical modeling techniques from experimental designs, generalized linear mixed models and predictive regression models. Build and maintain machine learning tools to improve existing processes and create opportunities for new model applications.
- Ph.D. (or MS with 4+ years of post MS experience) in Computer Science, Statistics, Mathematics, Geospatial Science or closely related field.
- 2 years of experience in using machine learning, artificial intelligence & statistical methodologies to solve research problems.
- Proficiency in Python
- 2 years of experience in using machine learning algorithms, artificial intelligence & statistical methodologies to solve research problems.
- Experience in developing statistical, and machine learning models for environmental and agronomical applications.
- Good knowledge on analytics algorithms and statistical concepts including multi-variate, computational statistics and matrix algebra.
- Highly competent in current technologies and open source solutions and be self-driven and able to deliver technical work on schedule within an environment.
- Experience analyzing and presenting complex data and proven problem-solving abilities.
- Good knowledge on analytics algorithms and statistical concepts including multi-variate, computational statistics and matrix algebra.
- Highly competent in current technologies and open source solutions and be self-driven and able to deliver technical work on schedule within an environment.
- Effective verbal communication of technical concepts and written documentation of progress and results.
- Need to have experience in handling version control tool like GitHub.
- Adhere to data science and coding best practices such as unit testing and packaging
- Must have Machine Learning experience and not solely remote sensing experience
- Experience working with agricultural/biological scientific data is highly desired.
- Experience working with various environmental data and categorizing environmental variabilities using geospatial model and approach is highly desired.
- Familiarity with deep learning architectures and techniques as well as constructing and evaluating these models.
- Drive for translating business problems into research initiatives that deliver business value.
- Creativity in defining challenging exploratory projects.
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