Data Scientist

Overview

• 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.
• 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.
  
  

• 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
  
  

• 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.
  
  

• 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.