JOB DESCRIPTION:

GIS ML Engineer will assist in developing and integrating machine learning models with geographic information systems (GIS) for the analysis, visualization, and processing of spatial data. This role requires collaboration with cross-functional teams to deliver data-driven solutions, automate geospatial tasks, and provide insights from spatial datasets.

JOB REQUIREMENTS:

• Bachelor’s degree in Geography, Computer Science, Data Science, or a related field. (Master’s degree is a plus)
• 3-5 years of experience with GIS software (e.g., QGIS, ArcGIS).
• Basic experience in machine learning, preferably using Python and popular libraries such as Scikit-learn, TensorFlow, or PyTorch.
• Experience working with geospatial data formats (e.g., shapefiles, GeoJSON, raster, vector).
• Proficient in programming languages such as Python and JavaScript (experience with geospatial libraries like GDAL, Fiona, or Rasterio is a plus).
• Understanding of machine learning concepts such as supervised learning, unsupervised learning, and model evaluation.
• Familiarity with geospatial database management systems (PostGIS, SQL).
• Experience with cloud platforms like AWS, GCP, or Azure, especially for geospatial applications, is an advantage.
• Good understanding of spatial analysis, spatial statistics, and data visualization techniques.
• Strong analytical and problem-solving skills.
• Excellent communication skills and the ability to work in a team-oriented environment.
• Basic knowledge of front-end web mapping frameworks (e.g., Leaflet, OpenLayers) is an added advantage.

• Assist in the design, development, and deployment of GIS-based machine learning models.
• Perform geospatial data collection, cleaning, and preprocessing for analysis.
• Develop custom algorithms to analyze large spatial datasets using machine learning techniques.
• Collaborate with senior engineers and data scientists to improve and optimize model performance.
• Integrate machine learning outputs into GIS platforms for mapping, visualization, and analysis.
• Develop and maintain geospatial databases and support data integration efforts.
• Contribute to the creation of tools or scripts to automate geospatial tasks using Python, R, or other relevant languages.
• Prepare technical documentation and reports summarizing project findings and recommendations.
• Stay up to date with advancements in GIS, machine learning, and data science technologies.