Geospatial Data Scientist

Website ceresimaging Ceres Imaging

Multispectral aerial imaging for agriculture

We are seeking a scientist with experience in machine learning, image processing, and geospatial analysis who can contribute to the development of products that extract agronomic insight from aerial imagery.


    • Analysis of geospatial datasets, primarily imagery, but also ground data in a variety of formats.
    • Development of tools that combine spectral imagery, weather, soil, ground samples, and other datasets to determine crop physiological traits, and translation to management recommendations to growers.
    • Development of machine learning models for pixel segmentation, object counting, and anomaly detection in imagery.
    • Contribution to development and maintenance of existing tools related to responsibilities listed above.

Desirable Experience/Skills

    • 3+ years of experience in a similar technical role
    • PhD in technical field, such as Remote Sensing, Earth Science, Atmospheric Science, Physics, Computer Science
    • Strong experience in a programming language used in scientific computing, such as python, MATLAB, Julia
    • Experience with tools in the scientific/geospatial python stack: numpy, scipy, pandas, scikit-image, scikit-learn, geopandas, rasterio
    • Experience with deep learning and neural networks, e.g., keras, tensorflow/theano
    • Ability to translate prototypes into reliable and maintainable code
    • Ability to iterate quickly
    • Ability to clearly document work
About Us:
Ceres Imaging is a venture-backed company developing technology that helps conserve water and fertilizer in farms. Specifically, we use aerial imagery and spectral image processing to monitor crop variables. The primary delivery mechanism for our imagery is a web application that allows customers to view the imagery of their fields over time (see the demo on our website).
We help the farmer use this data to improve crop management practices like fertilizer application, irrigation schedules, stress/problem detection, and other applications. Currently we have a substantial funding, and paying customers throughout the United States and Australia.

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