Job Description

1. Geoscience Data Integration & Spatial Analytics

• 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.

2. 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.

3. 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.

4. 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.

5. 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.

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.