Cognizant is hiring a Remote Sensing Data Scientist to transform complex satellite and aerial imagery into actionable insights that enhance agricultural decision-making at scale. You will work collaboratively with agronomists, biologists, field operations, and digital product partners to develop advanced remote sensing solutions.
What You'll Do
- Apply foundational and advanced remote sensing techniques—spanning image analysis, geospatial analytics, machine learning, and deep learning—to extract insights from multispectral, hyperspectral, and SAR datasets.
- Build, validate, and operationalize predictive and prescriptive analytics models that improve efficiency, reliability, and outcomes within agricultural operations.
- Design and maintain scalable pipelines for imagery ingestion, preprocessing, feature engineering, modeling, and deployment.
- Produce clear, comprehensive documentation covering problem framing, data requirements, modeling methodology, validation, and operationalization.
- Adhere to data science best practices, including code reviews, reproducibility, testing, and version control.
What We're Looking For
- Ph.D. or M.S. (with 4+ years of experience) in Remote Sensing, Geospatial Science, Data Science, Agronomy, Imagery & Robotics, or a related field.
- Hands-on expertise with remote sensing datasets and image processing techniques such as atmospheric correction, orthorectification, segmentation, and feature extraction.
- Proficiency with geospatial tools (e.g., GDAL, Rasterio, GeoPandas) and spatial data formats.
- Strong experience applying ML/DL approaches to imagery, including CNNs, semantic segmentation, object detection, time-series modeling, and emerging foundational models.
- Solid understanding of agricultural systems, crop development, environmental influences, and agronomic workflows, with the ability to contextualize imagery-derived insights.
- Experience using cloud environments (AWS, Azure, or GCP) for scalable data processing and deployment.
- Strong Python programming skills and familiarity with ML/DL frameworks (TensorFlow, PyTorch, OpenCV, scikit‑learn). Proficiency in SQL and best practices in version control, testing, and reproducible workflows.
- Strong communication, collaboration, curiosity, and a high sense of ownership, particularly when translating complex findings for non‑technical audiences.
Nice to Have
- Experience collaborating directly with agronomists, field teams, or scientists on agricultural analytics challenges.
- Familiarity with AI‑assisted coding tools for rapid prototyping.
- Applied experience deploying production-grade geospatial ML/DL models in cloud environments.
- Background working with large-scale imagery workflows in operational settings.
- Demonstrated ability to drive innovation through new modeling, feature extraction, or remote sensing methodologies.
Technical Stack
- Python, GDAL, Rasterio, GeoPandas, TensorFlow, PyTorch, OpenCV, scikit‑learn, SQL, AWS, Azure, GCP
Work Mode
This role follows a local-country work mode with locations in the United States.
