As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... The team tackles hard problems in a variety of spaces, such as matching, pricing, and geospatial ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... The team tackles hard problems in a variety of spaces, such as matching, pricing, and geospatial ...
Machine Learning Geospatial information
See Kearney, NE salary details
$17.89 - $20.26
16% of jobs
$21.44 is the 25th percentile. Wages below this are outliers.
$20.26 - $22.62
18% of jobs
The median wage is $24.99 / hr.
$22.62 - $24.99
16% of jobs
$24.99 - $27.36
21% of jobs
$29.02 is the 75th percentile. Wages above this are outliers.
$27.36 - $29.73
5% of jobs
$29.73 - $32.09
3% of jobs
$32.09 - $34.46
5% of jobs
$34.46 - $36.83
4% of jobs
$36.83 - $39.20
9% of jobs
$39.20 - $41.56
1% of jobs
$41.56 - $43.93
1% of jobs
$17
$27
$43
How much do machine learning geospatial jobs pay per hour?
As of Jun 7, 2026, the average hourly pay for machine learning geospatial in Kearney, NE is $27.46, according to ZipRecruiter salary data. Most workers in this role earn between $21.30 and $31.92 per hour, depending on experience, location, and employer.
What does a Machine Learning Geospatial professional do?
A Machine Learning Geospatial professional uses machine learning techniques to analyze and interpret geospatial data, such as satellite imagery, maps, and GPS data. Their work involves building and training models to detect patterns, make predictions, and solve spatial problems in fields like agriculture, urban planning, disaster response, and environmental monitoring. These professionals often collaborate with data scientists and GIS (Geographic Information Systems) specialists to extract actionable insights from large and complex geospatial datasets. Their skills are crucial for automating tasks such as image classification, land cover mapping, and object detection in geographic contexts.
What are some common challenges faced by Machine Learning Geospatial professionals when integrating spatial data into predictive models?
Machine Learning Geospatial professionals often encounter challenges such as managing large and complex spatial datasets, ensuring data quality and consistency, and handling spatial autocorrelation that can bias model results. Additionally, integrating diverse data sources—like satellite imagery, sensor data, and GIS layers—requires advanced pre-processing and domain knowledge. Collaborating with GIS analysts and domain experts is usually essential to develop robust models that provide actionable insights.
What is the difference between Machine Learning Geospatial vs GIS Analyst?
| Aspect | Machine Learning Geospatial | GIS Analyst |
|---|---|---|
| Required Credentials | Bachelor's or higher in Computer Science, Data Science, or related fields; knowledge of machine learning and geospatial data | Bachelor's in Geography, GIS, or related fields; proficiency in GIS software |
| Work Environment | Tech companies, data science teams, research institutions | Government agencies, urban planning, environmental firms |
| Industry Usage | Data-driven geospatial analysis, predictive modeling, AI applications | Mapping, spatial data management, spatial analysis |
Machine Learning Geospatial professionals focus on applying machine learning techniques to analyze geospatial data, often working with large datasets and developing predictive models. GIS Analysts primarily handle spatial data management, mapping, and analysis using GIS software. While both roles work with geospatial data, Machine Learning Geospatial roles emphasize data science and AI, whereas GIS Analysts focus on spatial information management and visualization.
What are the key skills and qualifications needed to thrive as a Machine Learning Geospatial specialist, and why are they important?
To thrive as a Machine Learning Geospatial specialist, you need a strong background in machine learning, geospatial analysis, programming (Python, R), and a relevant degree in computer science, geography, or a related field. Familiarity with GIS software (e.g., ArcGIS, QGIS), remote sensing tools, and cloud platforms like Google Earth Engine or AWS is typically required. Analytical thinking, problem-solving, and effective communication are vital soft skills for interpreting data and collaborating with multidisciplinary teams. These skills and qualities are crucial for developing accurate geospatial models and delivering actionable insights from complex spatial data.