Remote Sensing Data Scientist Job Town and Country Missouri USA,IT/Tech

Job Title – Remote Sensing Data Scientist

* Please note this role is not able to offer visa transfer or sponsorship now or in the future*

About the role

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.

In this role, you will:

Remote Sensing & Data Science

Apply foundations of remote sensing and advanced techniques in image analysis, geospatial analytics, machine learning, and deep learning to extract insights from complex imagery datasets (e.g., multispectral, hyperspectral, SAR).

Build, validate, and operationalize predictive and prescriptive analytics solutions that enhance efficiency, reliability, and decision making across agricultural operations.

Design and implement pipelines for imagery ingestion, preprocessing, feature extraction, and model deployment at scale.

Write comprehensive model documentation, including problem framing, data requirements, modeling approach, validation, and operationalization.

Adhere to data science best practices—peer review, code review, reproducibility, and version control.

Collaboration & Communication

Partner closely with agronomic scientists, biologists, field operations teams, and digital product teams to co-develop remote sensing solutions aligned with business needs.

Collaborate with global Data Science community to leverage shared tools, platforms, and modeling frameworks.

Communicate complex technical concepts, model performance, and insights to stakeholders clearly and effectively.

We strive to provide flexibility wherever possible. Based on this role’s business requirements, this is a remote position open to qualified applicants in the United States. Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programs.

The working arrangements for this role are accurate as of the date of posting. This may change based on the project you’re engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.

What you need to have to be considered

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, Geo Pandas) 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 GCP) for scalable data processing and deployment.

6. 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. Familiar with AI‑assisted coding tools and technologies for rapid prototyping.

7.

Soft Skills:

High sense of ownership,…


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