Data Scientist

What We Expect From You

1. Education & Experience: Ph.D. or M.S. (with 4+ years of experience) in Remote Sensing, Geospatial Science, Data Science, Agronomy, Imagery & Robotics, or related fields.
2. Imagery & Remote Sensing Expertise: Hands-on experience working with remote sensing datasets. Strong knowledge of image processing techniques (e.g., atmospheric correction, orthorectification, segmentation, and feature extraction). Experience with geospatial tools (e.g., GDAL, Rasterio, GeoPandas) and working with spatial data formats.
3. Machine Learning & Deep Learning: Proficiency in applying ML/DL methods to imagery: CNNs, semantic segmentation, object detection, time-series modeling, usage of foundational models. Strong understanding of statistical concepts, model evaluation, and uncertainty quantification.
4. Agriculture & Agronomy Background: Foundational understanding of agricultural systems, crop development, field operations, and agronomic principles. Ability to contextualize remote sensing insights within agronomic workflows (e.g., crop health diagnostics, field variability, environmental influences). Experience collaborating with agronomists or applying remote sensing to real world agricultural challenges is highly beneficial.
5. Cloud & Engineering Skills: Experience with cloud platforms (AWS, Azure, or Google Cloud Platform) for scalable data processing and deployment.
6. Programming Skills: Proficiency in Python and common ML/DL libraries (TensorFlow, PyTorch, OpenCV, scikit-learn). Experience with SQL for querying, transforming, and managing structured datasets. Strong coding discipline, including maintainable code, testing, version control (GitHub), and reproducible workflows. Familiary with AI assisted coding tools and technologies for rapid prototyping.
7. Soft Skills: High sense of ownership, curiosity, and motivation to deliver impactful analytic. Ability to explain technical findings to non-technical audiences. Strong collaboration and communication skills across diverse functional teams.