Please note there are specific application instructions required to be considered for this role
At Manulife John Hancock, we believe in investing in the future – starting with you. Our Co-op/Intern Program is your launchpad to success, offering the support, structure, and exciting opportunities you need to thrive and make your mark.
You will work on a variety of analytics projects with different use cases, contributing to the team of data scientists. The scope of the projects includes data collection, preprocessing, analysis, and model development. You will also contribute to creating actionable insights and project deliverables that benefit our team and the organization. Through these tasks, the co-op/intern will gain hands-on experience in applying advanced analytical methods to real-world problems.
Our co-op program offers more than just experience; you’ll join a vibrant community, participate in engaging events, receive competitive compensation with exam study support, benefit from personalized mentorship, and access into Canada’s largest actuarial network.

REQUIRED QUALIFICATIONS:

• Currently pursuing a master’s degree in Statistics, Computer Science, Data Science, Math, or related fields.
• Excellent coding skills using Python.
• Problem-solving abilities, teamwork, communication skills, and project management experience, all essential for a successful career in data science and machine learning.

PREFERRED QUALIFICATIONS:

• Familiarity with tools like Jupyter Notebooks, pandas, scikit-learn, TensorFlow, Databricks, and Azure Machine Learning Studio.
• Ability to demonstrate skills effectively in real-world projects.

• Key tasks involve exploring datasets, developing machine learning models using traditional ML techniques and generative AI methodologies, and validating and tuning these models to ensure accuracy and robustness.
• Specifically, the intern will use Python for coding and leverage Azure cloud services for data storage, processing, and model deployment.
• Deliver well-documented machine learning models, comprehensive data analysis reports, and organized code repositories.
• Prepare and present findings to the team and stakeholders.
• Contribute to improving existing processes and tools.
• Develop enhanced analytical skills, proficiency in traditional ML and GenAI methodologies.
• Improve collaboration and communication skills by working closely with our data science team.