Data Science Co-op/Intern

Job description

Position

Data Science Co-op/Intern Number of Position(s): 3 Duration: 4 Months Date: 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.

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.