Integrate and preprocess multisource geoscience datasets, including:
Geochemical data
Geophysical layers
Lithological maps
Structural GIS datasets
Utilize Python libraries (NumPy, Pandas, GDAL) and GIS tools (QGIS, ArcGIS) to automate spatial workflows and develop reproducible data pipelines.
Mineral Prospectivity & Potential Mapping
Build mineral prospectivity models using:
Convolutional Neural Networks (CNN)
Fuzzy Inference Systems (FIS)
Random Forest and other ensemble techniques
Generate mineral potential maps (MPMs) by integrating geological evidence layers and machine learning outputs.
Validate models using accuracy assessments, spatial crossvalidation, and performance metrics.
Machine Learning & Predictive Modeling
Design and implement ML models for classification, pattern recognition, and mineralization prediction (achieving up to 85% accuracy in past projects).
Optimize model performance, feature engineering, and hyperparameter tuning for geospatiallyinformed datasets.
Collaborate with domain experts to translate geological insights into MLready features.
Software Development & Automation
Develop automated geoprocessing tools and scripts for largescale spatial data handling.
Implement reproducible analytics using Python, SQL, and opensource geospatial frameworks.
Support deployment of analytical tools and dashboards for realtime monitoring and visualization.
Visualization & Reporting
Create clear, informative visualizations using Power BI, Matplotlib, and GIS platforms.
Prepare technical reports, workflow documentation, and presentations for internal and external stakeholders.
Qualifications
Technical Skills
Programming: Python, SQL
Machine Learning: CNN, FIS, Random Forest, supervised & unsupervised learning
Geospatial Tools: QGIS, ArcGIS, GDAL
Data Visualization: Power BI, Matplotlib
Web Technologies (Additional Advantage): HTML, CSS, PHP, jQuery
Experience 3-5 Years
Domain Expertise
Strong exposure to geoscience data integration, geological datasets, and mineral exploration workflows.
Experience in developing mineral prospectivity maps, handling mineral exploration datasets, and applying ML techniques to identify mineralization zones.
Ability to work with interdisciplinary teams including geologists, geophysicists, and remote sensing experts.
Professional Experience
Experience contributing to government or scientific organizations, including developing MLbased mineralization prediction models and automating geospatial workflows.
Prior roles in software or web development, demonstrating strong problemsolving, system design, and fullstack capabilities.
