Geospatial Data Scientist

jobs.earthbytelab.com; Stati Uniti; Remoto; USD 83.000 - 124.000; Tempo pieno

Vantaggi offerti da questo lavoro

Remote contract

US-based

Six-month term

Descrizione del lavoro

Earthbyte Lab is seeking a geospatial data analyst to collect, process, and analyze geospatial data across satellite imagery, spatial modeling, machine learning, and cloud workflows. This is a six-month remote contract for candidates based in the United States who are authorized to work there.

You will collaborate with cross-functional teams, develop scalable pipelines using Python and cloud services, and deliver data products such as dashboards and reports.

Competenze

• Bachelor’s or Master’s degree in GIS, remote sensing, geospatial or environmental science, computer science, or a related field.
• Strong Python skills and experience with geospatial data analysis.
• Understanding of machine learning concepts and applied modeling.
• Ability to work independently and communicate technical results clearly.
• Authorization to work in the United States.

Mansioni

• Process satellite imagery from sources such as Sentinel, Landsat, Planet, MODIS, and VIIRS.
• Develop spatial models for applications including vegetation, land cover, wildfire, drought, water stress, and soil moisture.
• Build machine learning workflows for tasks such as classification, segmentation, and prediction.
• Create scalable pipelines using Python, Google Earth Engine, and cloud services; combine imagery with sources such as LiDAR, radar, climate, and sensor data.
• Produce reusable scripts, notebooks, dashboards, maps, reports, and other data products.
• Contribute to research, prototypes, and solution development, and explain findings to collaborators or clients.

Conoscenze

Python

Geospatial data analysis

Machine learning concepts

Formazione

Bachelor’s or Master’s in GIS/remote sensing/ geospatial or environmental science

Strumenti

NumPy

Pandas

SciPy

scikit-learn

PyTorch

TensorFlow

GDAL

Rasterio

GeoPandas

Xarray

rioxarray

Descrizione del lavoro

Role overview

Collect, process, and analyze geospatial data to produce useful findings and data products. The work spans satellite imagery, spatial modeling, machine learning, and cloud-based workflows, with collaboration across teams to develop and deliver geospatial solutions. This is a six-month contract for candidates based in the United States who are authorized to work there.

Responsibilities

• Process satellite imagery from sources such as Sentinel, Landsat, Planet, MODIS, and VIIRS.
• Develop spatial models for applications including vegetation, land cover, wildfire, drought, water stress, and soil moisture.
• Build machine learning workflows for tasks such as classification, segmentation, and prediction.
• Create scalable pipelines using Python, Google Earth Engine, and cloud services; combine imagery with sources such as LiDAR, radar, climate, and sensor data.
• Produce reusable scripts, notebooks, dashboards, maps, reports, and other data products.
• Contribute to research, prototypes, and solution development, and explain findings to collaborators or clients.

Requirements

• Bachelor’s or master’s degree in GIS, remote sensing, geospatial or environmental science, computer science, or a related field.
• Strong Python skills and experience with geospatial data analysis.
• Understanding of machine learning concepts and applied modeling.
• Ability to work independently and communicate technical results clearly.
• Authorization to work in the United States.

Nice to have

• Familiarity with tools such as NumPy, Pandas, SciPy, scikit-learn, PyTorch, or TensorFlow, and geospatial libraries such as GDAL, Rasterio, GeoPandas, Xarray, or rioxarray.
• Experience with remote sensing preprocessing, spectral indices, cloud platforms, SAR, environmental time series, or land-cover mapping.
• Dashboard or web mapping experience, or work with agricultural, climate, or environmental data.

Benefits and work setup

Six-month, remote contract; candidates must be based in the United States.