Fall Co-op 2026 - Artificial Intelligence (AI)

Manulife•Toronto, ON

6d•Hybrid

About The Position

The AI Co-op Program at Manulife John Hancock is designed to provide students with hands-on experience applying data science and machine learning techniques to solve real-world business problems. Co-op students will work with modern tools and datasets to explore and prepare data, develop predictive models, and generate insights that drive decision-making. They will join a supportive team within a values-first culture that prioritizes career development and well-being. Co-ops will receive guidance from experienced practitioners and gain exposure to the end-to-end lifecycle of data science, machine learning, and emerging AI solutions in an enterprise environment. This program is a launchpad to success, offering support, structure, and exciting opportunities.

Requirements

Currently pursuing an undergraduate degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field
Experience with at least one programming language (e.g., Python, R, Java) and familiarity with data manipulation techniques
Familiarity with machine learning concepts and tools (e.g., scikit-learn, PyTorch, or similar frameworks)
Experience working with SQL or other data querying languages
Understanding of version control systems (e.g., Git) and collaborative coding practices
Interest in machine learning, data science, and emerging AI technologies

Nice To Haves

Exposure to machine learning techniques such as regression, tree-based models, neural networks, or time series methods
Familiarity with generative AI concepts (e.g., prompt engineering, retrieval-augmented generation)
Experience with cloud platforms (e.g., AWS, Azure, or Google Cloud) or data tools (e.g., Databricks, Spark)
Exposure to data visualization tools (e.g., Power BI, Tableau) and ability to present insights clearly
Strong problem-solving and communication skills, with the ability to apply technical solutions to business challenges

Responsibilities

Explore, prepare, and validate data while ensuring data quality and consistency for analysis and modeling
Build and evaluate machine learning models using a range of techniques, with thoughtful experiment design and documentation
Support feature engineering, model development, and performance optimization
Prototype generative AI solutions (e.g., prompting, retrieval-based methods) with a focus on safety and effectiveness
Develop reproducible code and workflows for data processing and modeling using tools such as Python and SQL
Communicate insights through clear visualizations, reports, and presentations for both technical and non-technical audiences