- Company Name
- SRG
- Job Title
- Data Scientist - Remote Sensing/Geospatial Science/Agronomy
- Job Description
-
**Job Title**
Data Scientist – Remote Sensing & Geospatial Science (Agronomy)
**Role Summary**
Develop and deploy machine‑learning solutions for agricultural remote‑sensing imagery. Build end‑to‑end pipelines for ingestion, preprocessing, feature extraction, and model deployment on cloud platforms, collaborating closely with agronomists and field teams to translate insights into operational decision‑making.
**Expectations**
- Ph.D. or M.S. with 4+ years post‑degree experience in remote sensing, geospatial science, data science, agronomy, imaging, or related disciplines.
- Proven ability to apply advanced ML/DL (CNNs, segmentation, detection, time‑series) to multi‑spectral, hyperspectral, and SAR data.
- Hands‑on experience with geospatial libraries (GDAL, Rasterio, GeoPandas) and cloud data‑processing (AWS, Azure, or GCP).
- Strong statistical understanding, model evaluation, and uncertainty quantification.
- Ability to integrate remote‑sensing outputs with agronomic workflows.
**Key Responsibilities**
- Design, implement, and maintain scalable pipelines for imagery ingestion, preprocessing, feature extraction, and model deployment.
- Build, validate, and operationalize predictive and prescriptive analytics models to improve agricultural efficiency and decision‑making.
- Write comprehensive model documentation and ensure adherence to data‑science best practices.
- Collaborate with agronomy, biology, field operations, and product teams to co‑develop solutions and tailor insights to field realities.
- Stay current on foundational models and emerging ML/DL methods applicable to remote‑sensing data.
**Required Skills**
- Programming: Python (TensorFlow, PyTorch, OpenCV, scikit‑learn), SQL.
- Geospatial: GDAL, Rasterio, GeoPandas, spatial data formats.
- Cloud: AWS, Azure, or GCP for scalable data processing and deployment.
- Machine Learning: CNNs, semantic segmentation, object detection, time‑series modeling, foundational-model application.
- Statistical analysis, model evaluation, uncertainty quantification.
- Strong communication and documentation abilities; teamwork across interdisciplinary groups.
**Required Education & Certifications**
- Ph.D. or M.S. in Remote Sensing, Geospatial Science, Data Science, Agronomy, Imaging, Robotics, or equivalent with ≥4 years of related experience.
- No specific certifications required beyond the advanced degree.
Creve coeur, United states
On site
Junior
18-02-2026