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Hybrid Data Scientist
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- Stay abreast of emerging technologies in big data, machine learning, and agriculture tech and advocate for their adoption where beneficial
- M.S. or above in Applied Statistics, Artificial Intelligence, Biostatistics, Computer Science, Data Engineering, Data Science, Engineering, Machine Learning, Physics, Software Engineering, or related highly quantitative fields.
- Must have strong experience in R or Python programming languages and their application to data wrangling, machine learning (e.g., TensorFlow, PyTorch), and data visualization,
- Experience and fundamental understanding of machine learning techniques (e.g., logistic regression, random forest, XGboost, SVMs, K-means, neural networks),
- Experience deploying machine learning models in production (e.g., CI/CD pipeline development; containerization using tools such as docker, podman, or Kubernetes; Git),
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