GIS Machine Learning Engineer
2 weeks ago
At IlmuOne Data, we are seeking a highly motivated and detail-oriented Junior GIS ML Engineer to join our team. As a key member of our data science team, you will play a crucial role in developing and integrating machine learning models with geographic information systems (GIS) for the analysis, visualization, and processing of spatial data.
Key Responsibilities:- 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.
- Bachelor's degree in Geography, Computer Science, Data Science, or a related field.
- 1-2 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.
- Passion for working with spatial data and applying machine learning to solve geospatial problems.
- Eagerness to learn and adapt to new technologies and methodologies.
- Strong attention to detail and ability to work independently with minimal supervision.
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