Data Scientist- Claims

Co-operators•Montreal, QC

1d•CA$69,660 - CA$116,100•Hybrid

About The Position

As a Data Scientist, you will contribute to initiatives by making clever use of customer-related data to help the organisation make data-driven decisions. Your role will involve various tasks, such as collecting and analyzing client data, building predictive models for customer behavior, creating segmentation strategies, implementing machine learning pipelines, deploying models into production systems, establishing MLOps workflows for model monitoring and automated retraining, and collaborating with cross-functional teams to translate data insights into actionable business strategies.

Requirements

Three years of experience in statistics, actuarial or data science.
A post-secondary degree in Mathematics, Statistics, Actuarial Science or a related discipline.
Proficient with statistical programming languages and have experience working with large data volumes.
Strong understanding of P&C Insurance claims concepts.
Experience communicating complex information to diverse audiences.
Proficiency in English.

Nice To Haves

Master’s degree, ACAS/ACIA or FCAS/FCIA designation.
Experience working with R or Python and SQL.
Experience with Databricks, MLflow or PySpark.
Strong background using statistics to build and validate predictive models.
Experience with model lifecycle and MLOps.
French language proficiency.

Responsibilities

Understanding business objectives and help stakeholders implement data-driven initiatives that maximize customer satisfaction, retention, and profitability.
Developing advanced analytics solutions to solve client-related business problems using data science, including programming, statistical techniques, machine learning modeling, and predictive forecasting methods.
Executing comprehensive data exploration, extraction, cleaning, reconciliation, and preparation processes from multiple client touchpoints to build robust analytics foundations.
Designing and implement MLOps pipelines for model deployment, monitoring, and automated retraining to ensure continuous model performance and reliability in production environments.
Communicating actionable insights and recommendations to influence client strategy, marketing decisions, and product development through compelling visualizations and presentations.
Contributing to strategic projects such as customer lifetime value modeling (CLV), churn prediction, personalization engines, and developing client success KPIs that drive profitable growth.