Data Services Co-op

Role Overview

The Data Services Co-op supports the processing, refinement, and scaling of geoscience data to accelerate data readiness for clients and drive AI adoption in the mining industry. Positioned at the intersection of geoscience, data management, and modern data workflows, this role collaborates with cross-functional technical teams to optimize data pipelines, improve quality control standards, and help build the data foundation required for advanced AI/ML applications in the geoscience industry.

Core Responsibilities

• Data Processing & “Moonshot” Projects: Assist in collecting, cleaning, structuring, and standardizing diverse, large-scale geoscience datasets (e.g., spatial, geochemical, geophysical, and drillhole data) to support advanced analytics and AI-driven initiatives.
• Process & Workflow Improvements: Contribute to the design, testing, and automation of data ingestion and QA/QC workflows to enhance data processing efficiency, operational scalability, and data accuracy.
• AI Tooling & Testing Support: Support the evaluation, beta testing, and deployment of emerging AI/ML feature sets and internal data tools, providing practical feedback on data quality, usability, and edge cases.
• Documentation & Team Collaboration: Track project milestones, maintain documentation for standardized data protocols, and communicate progress, technical blockers, and updates across internal technical teams using standard operational tools.

Qualifications & Requirements

• Education: Currently enrolled in a post-secondary program in Geoscience, Earth Sciences, Data Analytics, Computer Science, or a related hybrid field (eligible for a formal co-op placement).
• Data & Technical Aptitude: Comfort working with structured datasets, data manipulation, and spatial/geoscience data concepts (e.g., GIS, relational databases, or tabular data formats).
• Analytical Mindset: Strong attention to detail with an interest in scalable data management, process optimization, and machine learning applications in the natural resources sector.

Assets

• Practical exposure to scripting languages (e.g., Python, SQL) for data manipulation and automation.
• Familiarity with cloud data platforms, version control tools, or data pipeline concepts.