Senior Geo Data Scientist

VRIFY

2d • Remote

About The Position

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 productionize 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.

Requirements

• 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, TensorFlow, 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)

Nice To Haves

• Experience applying deep learning architectures (e.g., Vision Transformers, GNNs) to geospatial problems
• Experience with geospatial tools and libraries (e.g., GeoPandas, Rasterio, GDAL, QGIS, ArcGIS)
• Experience with Google Earth Engine or similar platforms
• Experience with modern AI tooling (e.g., Hugging Face)
• Background in mineral exploration, geology, or earth sciences

Responsibilities

• 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

Benefits

• Health Benefits: Extensive coverage, medical, dental, and vision plans.
• Paid Time Off (PTO): Including vacation days, sick days, personal days, public holidays plus extra time during holiday season.
• Work-Life Balance: Flexible work hours, remote work options plus option to use work space in Downtown Vancouver (free snacks & gym).
• Professional Development: Career growth program to help our team unlock their potential and advance their career.
• Performance Bonuses
• Wellness Programs: Fitness allowance, work from home allowance, mental health support.
• Retirement Plan (RRSP/DPSP)

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 11-50 employees