Senior Geo Data Scientist

VRIFY is positioned at the forefront of the mining industry's transformation, leveraging cutting-edge AI to revolutionize mineral exploration. With a focus on AI drill targeting, VRIFY is expanding its capabilities by synthesizing vast amounts of geological information. This integration enhances the precision and efficiency of exploration strategies, offering our clients innovative solutions that depart from traditional methods.

Our mission extends beyond technological advancement. We are committed to transforming how mining companies engage with investors, aiming to foster a more transparent and accountable mining investment ecosystem. VRIFY's technology enables immersive 3D and 360° presentations that provide investors with a vivid and detailed visual context, making complex geological data accessible and engaging.

As we continue to refine our AI‑driven solutions, VRIFY remains dedicated to driving innovation and excellence in the mining sector, ensuring our clients and investors are well‑equipped to succeed in a rapidly evolving market.

As a Senior Geo Data Scientist, you will be at the forefront of developing advanced machine learning models that power our platform, transforming complex geospatial data into actionable predictions that directly inform mineral exploration decisions.

In this role, you will design, build, and scale machine learning systems and data pipelines for geospatial data, working across the full lifecycle from data ingestion and feature engineering to model development, evaluation, and deployment. You will develop and product ionize models on high‑dimensional spatial datasets, ensuring robust workflows, reproducibility, and performance, while integrating modern AI approaches into scalable systems.

The ideal candidate has a strong foundation in machine learning and geospatial data, with experience building production‑grade models and working with large‑scale spatial datasets. You bring a systems‑level mindset, strong technical ownership, and a collaborative approach, and you will play a key role in shaping best practices, contributing to technical direction, and advancing how we apply machine learning to geospatial problems.

• Design and deploy machine learning models for geospatial applications, including deep learning architectures (e.g., Vision Transformers, GNNs) applied to high‑dimensional raster and spatial datasets.
• Develop scalable data pipelines for geospatial data, including preprocessing, feature engineering, sampling strategies, and spatial cross‑validation.
• Build and maintain end‑to‑end ML workflows, ensuring reproducibility, performance optimization, and reliable generation of actionable predictions.
• Develop custom geospatial models that capture real‑world spatial patterns to improve prediction accuracy and support decision‑making.
• Engineer geospatial features that reflect spatial relationships and domain‑specific characteristics to enhance model performance.
• Apply and advance model interpretability techniques to understand spatial patterns and quantify feature influence in complex ML models.
• Use tools like Google Earth Engine and Hugging Face to process large‑scale geospatial data and integrate modern AI models into production workflow.

• BSc, MSc, or PhD in Computer Science, Engineering, Geoscience, or a related field, or equivalent practical experience.
• 5+ years of experience in machine learning, data science, or software development, including production ML systems.
• Strong experience with modern machine learning frameworks (e.g., PyTorch, Tensor Flow, JAX, scikit‑learn).
• Deep understanding of machine learning architectures (e.g., transformers, vision transformers) and approaches such as clustering and ensemble methods.
• Strong programming experience in at least one high‑level language (e.g., Python).
• Experience building and deploying machine learning models in production environments.
• Experience working with geospatial data (e.g., raster data, satellite imagery, spatial datasets).

• Experience applying deep learning architectures (e.g., Vision Transformers, GNNs) to geospatial problems.
• Experienc…