Data Scientist - Geoscience and Mineral Exploration

Data Scientist

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. 

Experience Level

Mid Level