Data Scientist (In-Office)

At GMS, we've been helping Canadians for more than 75 years to get the health and travel insurance they want and need. The same pioneering spirit that started our story is what drives us to do things differently today. Insurance, honestly, is our promise, and it's what we do at GMS. We care about our customers, our community and each other. As a non-profit organization, we're proud to reinvest our profits into the health of the communities we serve and that have supported us since 1949.

We want our employees to feel good about coming to work and being in a workplace that promotes flexibility, growth and a healthy work-life balance. If you'd like to be part of a team that truly takes care of our customers, our communities, and each other, this could be your chance.

Here's the role

We are seeking a highly skilled Data Scientist to join our Pricing & Underwriting department. This role focuses on developing predictive analytics models, machine learning algorithms, simulations, and advanced statistical techniques to enhance pricing strategies, assess risks, and improve underwriting processes. The ideal candidate has a strong analytical background, expertise in statistical modeling, and experience in applying AI-driven solutions to the insurance industry.

Position Responsibilities

• Develop and implement predictive models to improve pricing strategies and risk assessment.
• Design and deploy machine learning algorithms for claims prediction and customer segmentation.
• Utilize simulation techniques to assess various pricing scenarios and optimize premium structures.
• Apply advanced statistical modeling techniques such as GLMs, Bayesian inference, and time-series forecasting.
• Collaborate with actuaries, underwriters, and business stakeholders to translate complex data insights into actionable strategies.
• Process and analyze large datasets to identify trends and correlations affecting policy pricing and risk selection.
• Build data-driven underwriting frameworks to improve accuracy and efficiency in policy approvals.
• Implement and maintain machine learning pipelines for continuous model training and evaluation.
• Communicate findings and recommendations to senior management through reports, dashboards, and presentations.
• Stay updated on emerging trends in AI, data science, and healthcare insurance analytics.

Competencies

• Analytical Thinking: Strong data and trend analysis skills to identify gaps, and opportunities; quickly and effectively solves problems and recalls information to discuss the historic trends of past decision making.
• Creativity and Innovation: Develops fresh ideas to solve problems and discover opportunities; able to grasp technical concepts, absorb new ideas and concepts quickly; has strong problem identification and problem resolution skills.
• Problem Solving: Actively explores and analyzes options and solutions to make effective customer and business decisions; considers causal relationships and impacts of risk decisions within area of knowledge and expertise.
• Quality Orientation: Sets high expectations for self; ensures accurate delivery of work across the team; conducts extensive discussions with peers, business units and external business partners to ensure assumptions are complete.

Education & Experience

• Bachelor or Masters Degree in Data Science, Statistics, Mathematics, Actuarial Science, Computer Science, or a related field.
• 3+ years of experience in data science, preferably within the insurance, healthcare, or financial sectors.
• Strong proficiency in Python, R, SQL, and data visualization tools such as Tableau or Power BI.
• Hands-on experience with machine learning frameworks (e.g., TensorFlow, Scikit-learn, XGBoost).
• Expertise in statistical modeling, including GLMs, decision trees, clustering, and deep learning techniques.
• Experience working with large datasets and cloud-based platforms (AWS, Azure, or GCP).
• Solid understanding of health insurance pricing, risk modeling, and underwriting principles would be considered an asset.
• Experience with actuarial pricing models and working with actuaries.
• Familiarity with regulatory compliance in health insurance data analytics.
• Knowledge of Monte Carlo simulations and optimization techniques.
• Proficient in big data technologies (Spark, Hadoop) would be considered an asset.

Are we a fit?
If you think so, please apply by March 13, 2025. We'd love to reach out to everyone who applies, but we just don't have enough hands! If you're selected for an interview, we'll be in touch. If not, please consider us again in the future.