Position Title: Co-op Student - Group Actuarial (Data & Analytics)
Reports To: Director, Group Insurance Data Insights & Analytics
Department: Group Actuarial
Term: Temporary Full-Time, Four (4) Month Contract
Work Term: May 5, 2025 - August 22, 2025


Work Arrangements: This is a hybrid role. You will work in our office in Waterloo, ON a minimum of two (2) assigned, consecutive days every other week, plus a fifth (5th) assigned day per month. You are welcome to work from the office more than the minimum requirement and there may be some roles that are required to work in our office more than the minimum requirement.

The Opportunity: Are you ready to kickstart your career and gain hands-on experience on a dynamic and supportive team?

At Equitable, we believe in nurturing talent and providing opportunities for growth. As a Co-op student, you'll have the unique chance to work alongside experienced professionals who are leaders in their fields. You'll be part of a collaborative team where your contributions are valued, and you'll receive mentorship and guidance to help you develop your skills and achieve your career goals.

Come join one of the Waterloo Area's and Southwestern Ontario's Top Employers for 2024!

What you will be doing:

• Apply machine learning at scale in an anomaly detection project for Group Benefits
• Develop innovative algorithms that provide best-in-class solutions for fraud detection of claims
• Brainstorm and create powerful visualizations in Power BI that makes data accessible and understandable
• Utilize the latest advancements in GenAI to produce automated alert leveraging the developed prediction algorithms
• Contribute with development best practices and experiment with new ideas
• Work collaboratively with cross-functional teams to identify creative solutions for data targeting, and plan and execute on a project from concept to production

What you will bring:

• Candidates must be currently enrolled in a post-secondary program that includes a Co-operative (Co-op) work component
• 2+ years of data science or data analytics education
• Strong and fundamental understanding of statistics, including knowledge of probability theory, hypothesis testing, regression analysis, and data modeling.
• Solid understanding of Machine Learning (ML) concepts and its application in a variety of use cases such as classification, regression, clustering, and anomaly detection.
• Experience developing ML solutions including preprocessing, feature engineering, model selection, training, validation, and deployment.
• Good foundation in SQL for data manipulation, querying, and analyzing data to support machine learning and data science initiatives.
• Strong programming skills in python (preferred), including relevant statistical, machine learning, and deep learning libraries (pandas, numpy, stats models, scikit-learn, tensorflow, etc.)
• Skills in data visualization (Power BI)
• Strong communication skills, including the ability to explain complex ideas to a non-technical audience

What's in it for you:

• A healthy work-life balance with employee wellness top of mind
• We value open, honest communication between team members, challenging each other to continually improve
• Employee resource groups that support an inclusive work environment
• An onsite full-service cafeteria with a variety of daily options
• Regular EQ Together events focused on company togetherness and collaboration
• Compensation package includes:

• In Lieu of Benefits - 4% added to each bi-weekly pay
• In Lieu of Vacation - 4% added to each bi-weekly pay

• Employer provided equipment
• Opportunity to participate in our buddy program dedicated to Co-op students
  

This role is open due to an existing vacancy.

To learn more about Equitable, we encourage you to explore our organization.

At Equitable, we are committed to providing equal access to employment opportunities across our organization. Please contact our HR team at careers@equitable.ca if you would like to receive our job postings in an alternative format or require an accommodation with the application process.

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