Remote Sensing Data Scientist : Creve Coeur, MO_Hybrid_Wrole

W2 Role

Candidate must show the DL copy on video at the time of submission.

Remote Sensing Data Scientist

Creve Coeur, MO - Hybrid

8 years

We are seeking a highly skilled Remote Sensing Data Scientist with deep expertise in remote sensing principles, machine learning, deep learning, geospatial analytics, and image processing to join our dynamic team. The ideal candidate has a proven track record working with remote sensing data (satellite, aerial imagery) and can translate imagery into actionable insights through advanced modeling techniques.

• 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.
• 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, Geo Pandas) and working with spatial data formats.
• 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.
• 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.
• Cloud & Engineering
  
  Skills:
  
  Experience with cloud platforms (AWS, Azure, or Google Cloud Platform) for scalable data processing and deployment.
• Programming
  
  Skills:
  
  Proficiency in Python and common ML/DL libraries (Tensor Flow, PyTorch, OpenCV, scikit learn). Experience with SQL for querying, transforming, and managing structured datasets. Strong coding discipline, including maintainable code, testing, version control (Git Hub), and reproducible workflows. Familiarity with AI‑assisted coding tools and technologies for rapid prototyping.
• Soft Skills:
  
  High sense of ownership, curiosity, and motivation to deliver impactful analytics. Ability to explain technical findings to non‑technical audiences. Strong collaboration and communication skills across diverse functional teams.