TerraPrecise is tackling the unprecedented financial pressure farmers and their insurers face from increasingly volatile weather by providing hyper-localized, prescriptive alerts that empower them to make critical decisions, protect their investments, and improve their financial stability.
Requirements
- Strong proficiency in Python and its scientific computing libraries (e.g., Pandas, NumPy, SciPy).
- Demonstrable experience in data analysis, statistical modeling, and handling large, complex datasets.
- Comfortable working in a Linux environment and using command-line tools.
- Experience with front-end frameworks (e.g., React, Vue.js) and back-end frameworks (e.g., FastAPI, Django, Node.js).
- Familiarity with cloud platforms (AWS, Google Cloud) and database management (e.g., PostgreSQL/PostGIS).
- Understanding of or experience with parallel and concurrent processing to handle large-scale data workflows.
- Experience with geospatial data libraries (e.g., GDAL, Rasterio, GeoPandas)
Responsibilities
- Collaborate on the design and implementation of the core data fusion and alerting engine, integrating diverse datasets including but not limited to satellites, IoT, mesonet, and the National Weather Service data.
- Develop and build out the user-facing, mobile-first dashboard to visualize complex geospatial and time-series data in an intuitive way.
- Implement the back-end logic for delivering prescriptive, customized recommendations via SMS and email alerts based on a farm's specific region, soil, crops, and topography.
- Design and build a scalable data ingestion pipeline capable of handling large volumes of environmental data efficiently.
- Contribute to the development and validation of analytical and or statistical models that translate raw data into actionable agronomic insights, such as drought stress warnings or flood impact assessments.
- Participate in refining the product by incorporating feedback from an initial pilot program.
Other
- Currently pursuing a Master's or PhD degree in Computer Science, Engineering, Data Science, Environmental Science, or a related quantitative field.
- Excellent communication skills and the ability to work collaboratively in a remote setting.
- Strong problem-solving skills and a genuine passion for learning and building.
- Personal or professional experience in agriculture, agronomy, or a related field.
- A portfolio of work or a GitHub profile showcasing past research or development projects.
