Data Services Co-op

Job summary

The role involves processing, structuring, and standardizing large-scale geoscience datasets to support AI-driven initiatives. It also includes contributing to the automation of data workflows and supporting the testing of internal AI/ML tools.

Job details

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.

What you’ll do

The role involves processing, structuring, and standardizing large-scale geoscience datasets to support AI-driven initiatives. It also includes contributing to the automation of data workflows and supporting the testing of internal AI/ML tools.

Requirements

Candidates must be currently enrolled in a post-secondary program related to Geoscience, Data Analytics, or Computer Science. Strong analytical skills and familiarity with data manipulation, scripting languages, and spatial data concepts are required.

Listed skills

• SQL · Preferred
• Machine learning · Preferred
• data management · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Geoscience
• Data management
• Data processing
• Python
• SQL
• GIS
• Data pipelines
• Machine learning
• QA/QC
• Data analytics
• Automation
• Cloud data platforms
• Version control
• Spatial data
• Geochemical data
• Geophysical data
• Workflow Management
• Advanced Analytics
• AI Adoption
• Artificial Intelligence
• Data Analysis
• Version Control
• Computer Science
• Data Processing
• Data Management
• Data Manipulation
• Data Quality
• Relational Databases
• Geochemistry
• Earth Science
• Geophysics
• Scalability
• Python (Programming Language)
• Machine Learning
• Milestones (Project Management)
• Process Optimization
• SQL (Programming Language)
• Tabular Data
• Tooling
• Usability
• Scripting
• Data Ingestion
• Quality Control
• Technical Acumen
• Detail Oriented
• Data Pipelines
• Applied Machine Learning

Job areas

• Data & Analytics
• Science & Research
• Technology
• Software
• Energy
• Data Services Manager
• Data Scientist
• Systems Analysts
• Data Scientists