Data Services Co-op

VRIFY; Vancouver; On-site; CAD 25,000 - 30,000; Part time

Job summary

VRIFY is seeking a Data Services Co-op in Vancouver to support processing, refinement, and scaling of geoscience data for AI readiness across mining projects.

You will collaborate with cross-functional teams to optimize data pipelines, improve QA/QC standards, and help build the data foundation for advanced AI/ML applications in geoscience. This role emphasizes hands-on data manipulation, Python/SQL scripting, and exposure to cloud platforms.

Qualifications

• Currently enrolled in a post-secondary program in Geoscience, Earth Sciences, Data Analytics, Computer Science, or related hybrid field.
• Comfort working with structured datasets and spatial/geoscience data concepts (GIS, databases).
• Strong analytical mindset with interest in scalable data management and ML in natural resources.

Responsibilities

• Data Processing & Moonshot projects: collect, clean, structure, and standardize geoscience datasets to support analytics and AI initiatives.
• Process & workflow improvements: design, test, and automate data ingestion and QA/QC workflows for efficiency and accuracy.
• AI tooling & testing support: assist in evaluating, beta testing, and deploying AI/ML features and data tools, with feedback on data quality and edge cases.
• Documentation & team collaboration: track milestones, maintain standard data protocols, and communicate progress across internal teams.

Skills

Python

SQL

GIS

Data manipulation

Education

Post-secondary program in Geoscience/Data Analytics/CS

Tools

Cloud platforms

Version control

Data pipelines

Job description

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.

Note: Preference will be given to candidates who are eligible for funding under the Student Work Placement Program (SWPP).