Manager, Machine Learning Operations (MLOps)

Wawanesa Insurance•Calgary, AB

25d•CA$140,000 - CA$180,000•Hybrid

About The Position

The Manager, Machine Learning Operations (MLOps) contributes to Wawanesa’s success by bringing your passion for predictive modeling and machine learning. In this role, you will lead the Machine Learning Operations (MLOps) function, ensuring scalable, reliable, and cost-effective deployment of machine learning solutions across the enterprise. The manager is leading a team responsible for taking machine learning applications to support decision making across the organization from concept to production, with accountability for platform architecture, production reliability, governance, and lifecycle management. Wawanesa offers a hybrid work environment that offers flexibility to our employees in balancing in-office (2 days per week OR 15 hours per week in a Wawanesa office) and remote work. You may work from any of the following locations: Winnipeg, MB; Calgary, AB; Toronto (North York), ON. The Wawanesa Mutual Insurance Company (“Wawanesa Mutual”), founded in 1896, is one of Canada’s largest mutual insurers, with over $3.5 billion in annual revenue and assets of $10 billion (CAD). Wawanesa Mutual, with its National Headquarters in Winnipeg, is the parent company of Wawanesa Life, which provides life insurance products and services throughout Canada, and Western Financial Group, which distributes personal and business insurance across Canada. Wawanesa proudly serves more than 1.7 million members in Canada, and we are home to more than 3,300 employees distributed across the Canadian regions and communities where we operate. We give back to organizations that strengthen communities, donating more than $3.5 million annually to charitable organizations, including over $2 million annually in support of people on the front lines of climate change. We are also proud to be recognized as one of Manitoba’s Top Employers. We are currently looking for dedicated, driven, and enthusiastic individuals who thrive in an environment that welcomes change and are looking for an opportunity for diverse experience and advancement on a growing team.

Requirements

A minimum of five years of experience in developing and deploying enterprise-scale machine learning solutions, and one year of people leadership with proven ability to build high performing teams.
Strong business acumen with advanced analytical and problem-solving skills, with the ability to recognize, and identify critical issues.
Excellent interpersonal, presentation and communication skills, with the ability to effectively convey complex ideas in a simple, persuasive, and eloquent manner.
Comfortable confronting difficult issues and diplomatic in delivery of challenging messages.
Ability to establish and maintain good relationships with key stakeholders.
Advanced planning and organizing skills, with the ability to manage and prioritize a busy workload and multiple projects.

Nice To Haves

Knowledge and experience in the insurance industry is considered an asset.

Responsibilities

Work directly with organizational leaders to introduce advanced analytics to all functions within the organization, and to foster the advancement and adoption of analytic assets.
Develop and advance analytic standards and processes that enable all aspects of advanced analytics including applied research, proof of concept, deployment of production grade ethical machine learning models, model monitoring and measurement.
Own and evolve the enterprise MLOps platform strategy, including CI/CD for ML, model registry, feature management, orchestration, monitoring, and observability frameworks.
Establish standardized deployment patterns and infrastructure-as-code practices to reduce bespoke solutions and increase reuse across teams.
Model lifecycle governance from development through validation, deployment, monitoring, retraining, and retirement.
Identify and organize educational initiatives aimed at the development of overall inter- and intra-departmental knowledge.
Keeps abreast with new tools, algorithms and techniques in machine learning and works to implement them in the organization.
Perform other duties as assigned.