Senior Data Scientist

This is a fully remote position, open to applicants in Canada.

📋 Description

• Design, develop, and evaluate machine learning solutions across various insurance sectors, including claims, underwriting, sales, and marketing.

• Take ownership of feature engineering utilizing large-scale insurance datasets.

• Lead the processes of model selection, training, validation, and performance optimization.

• Manage highly imbalanced datasets, weak labels, and proxy targets.

• Convert business rules into machine learning features and hybrid rule-ML systems.

• Ensure model explainability, stability, and governance in accordance with insurance and regulatory standards.

• Serve as the client-facing data scientist and oversee client relationships.

• Prepare demonstration and sprint review documentation.

• Translate complex business challenges into specific analytical use cases, modeling strategies, and delivery plans.

• Engage in discussions regarding architecture and solution design, model walkthroughs, UAT discussions, and the definition of model acceptance criteria.

• Communicate potential risks, dependencies, and trade-offs in delivery.

• Collaborate with offshore teams to guarantee the quality of delivery.

• Work alongside data engineering and platform teams to develop analytical data models and feature stores.

• Ensure that models are production-ready.

• Contribute to the design of MLOps, including model versioning, monitoring, and retraining strategies.

• Assist in establishing deployment patterns on contemporary analytics platforms while ensuring adherence to enterprise standards for scalability, reliability, and auditability.

⛳️ Requirements

• 5–8 years of experience in advanced analytics or data science.

• Strong preference for experience in the insurance domain (P&C, Life, Health, Group Benefits, or Claims).

• Demonstrated experience in delivering end-to-end machine learning solutions in production settings.

• Proficient hands-on experience with Python, including libraries such as pandas, scikit-learn, and XGBoost / LightGBM.

• Knowledge of statistical modeling and machine learning algorithms, including classification, regression, and segmentation.

• In-depth understanding of feature engineering on transactional and behavioral data.

• Comprehensive knowledge of techniques for handling imbalanced classification.

• Strong grasp of model evaluation, stability, and monitoring for drift.

• Experience with SQL and large-scale datasets.

• Familiarity with working in offshore or distributed data science teams.

• Excellent storytelling abilities to convey complex analytical ideas to non-technical stakeholders, onsite leadership, and clients.

• Comfortable collaborating across different time zones and within a matrix delivery framework.

• Familiarity with modern machine learning platforms, cloud data environments, or analytics fabrics is a plus.

• Preferred or nice-to-have experience with model governance, regulatory expectations, Explainable AI (XAI) techniques, and MLOps pipelines and CI/CD for analytics.

🏝️ Benefits

• Work From Home / remote work