Data Science Co-op/Intern

Position Details

Position: Data Science Co-op/Intern Number of Positions: 3 Duration: 4 Months Dates: January 4, 2027 - April 30, 2027 Location: Hybrid in Ottawa, CANADA

Education Requirements

Currently pursuing a Bachelor's or Master's degree, or a College diploma, at an accredited Canadian university or college in a relevant technical discipline. Preferred fields of study include Data Science, Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, Artificial Intelligence, Statistics, Mathematics, or other related programs.

Responsibilities

You will:

• Design, develop, and evaluate AI-powered applications leveraging Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), and intelligent workflow automation technologies.
• Collaborate with engineering teams to analyze workflows, identify automation opportunities, and translate business requirements into practical AI-driven solutions.
• Develop software applications, APIs, and automation tools using Python and AI-assisted development platforms such as Cursor AI and GitHub Copilot.
• Analyze engineering and operational data to generate reports, dashboards, visualizations, and actionable insights that support data-driven decision making.
• Participate in Agile development activities, design reviews, AI adoption initiatives, workshops, and knowledge-sharing sessions across global engineering teams.

Required Qualifications

You must have:

• Strong programming experience in Python and familiarity with software development, automation frameworks, APIs, and application design principles.
• Knowledge of Generative AI concepts, including Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, and agentic workflows.
• Experience using modern AI-assisted development tools such as Cursor AI, GitHub Copilot, or similar platforms to improve software development productivity.
• Understanding of data analytics, statistics, data modeling, and visualization techniques obtained through academic coursework, projects, or practical experience.
• Strong analytical, problem-solving, communication, collaboration, and continuous learning skills with a demonstrated interest in AI and automation technologies.

Nice-to-Have Qualifications

• Experience building AI-powered applications using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar technologies.
• Exposure to AI guardrails, LLM evaluation, observability, tracing, monitoring solutions, and AI application lifecycle management practices.
• Experience with enterprise application development, dashboards and visualization platforms (Grafana, Splunk, Power BI), Git-based development workflows, CI/CD pipelines, or participation in hackathons, research projects, or AI-related extracurricular activities.