Leverage your statistical knowledge to select and apply model evaluation metrics across Regrow’s suite of models and data products. Analyze model performance and identify areas for improvement.
Develop and implement data pipelines for efficient data ingestion and processing. Use these pipelines to create high-quality training and validation data for model development and evaluation.
Work with the Data Science and Data Engineering teams to assess the accuracy and quality of landscape scale data products (crop type, agricultural practice adoption, etc).
Source ground truth and derived data for model training and validation. Data can include cropping and agricultural practices, irrigation timing, and biophysical data (eg crop residue cover).
Train, evaluate, and optimize ML/DL models to achieve desired performance metrics. Communicate findings effectively to technical and non-technical audiences.
Skills Required
Machine Learning
Python
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