Data Scientist at Allstate Insurance Company in Canadian Head Office

Job description

• Job Description
• In this role, you’ll have a front-row seat to the entire insurance ecosystem—claims, underwriting, pricing, and even broader business areas like HR and marketing. You won’t just observe; you’ll work on high-impact projects that drive real results. From day one, you’ll dive into stakeholder data, blending quantitative insights with business expertise to build predictive models that shape smarter decisions. Your work will go beyond theory—every model you create will deliver actionable insights and innovative solutions that move the needle for the company. You’ll collaborate closely with fellow data scientists and business leaders, turning ideas into strategies that improve performance and strengthen the bottom line.

Responsibilities

• Lead the end-to-end development, deployment, and monitoring of predictive models supporting Lifetime Value (LTV), Loss Cost, pricing, retention, and profitability initiatives within the insurance business.
• Translate complex business problems into scalable analytical solutions by partnering closely with stakeholders across Product, Pricing, Marketing, Claims, and Finance.
• Develop, validate, and maintain advanced machine learning, statistical, and predictive models, ensuring strong model performance, interpretability, and business value.
• Deploy and operationalize models using Azure Machine Learning and modern MLOps practices, ensuring reliability, scalability, and governance compliance.
• Communicate analytical findings, model insights, and recommendations to technical and non-technical audiences, enabling data-driven decision making.
• Provide thought leadership on customer value, risk, profitability, and growth opportunities through advanced analytics and data science innovation.

Requirements

• Demonstrated experience in machine learning and analytics, ideally within a professional services firm or similar environment, with a strong background in implementing and optimizing machine learning algorithms to solve regression, classification, and segmentation problems
• Proven experience developing and deploying machine learning and statistical models in a business environment, ideally within Property & Casualty (P&C) insurance, financial services, or other data-intensive industries.
• Advanced programming skills in Python and SQL, with hands-on experience using modern machine learning libraries and data science frameworks.
• Experience working with Azure Machine Learning or other cloud-based analytics platforms, and production model deployment workflows.
• Strong understanding of data preparation, feature engineering, model validation, experimentation, and performance monitoring best practices.
• Excellent communication and stakeholder management skills, with the ability to explain complex analytical concepts to business audiences and senior leaders.
• Working knowledge of SAS and experience supporting or modernizing legacy analytical environments is considered an asset.
• Experience with MLOps, model governance, responsible AI practices, and production-scale machine learning solutions is considered a strong asset.

Skills

Required

• Machine learning
• Analytics
• Python
• SQL
• Azure Machine Learning
• Model deployment
• Data preparation
• Feature engineering
• Model validation
• Experimentation
• Performance monitoring
• Communication

Preferred

• SAS
• MLOps
• Model governance
• Responsible AI
• Production-scale machine learning

Benefits

• Flexible Work Arrangements.
• Employee discounts (15% on auto and property insurance, plus many other products and services).
• Good Office program (receive up to $400 back after purchasing office equipment).
• Student Loan Payment Matching Program for Government Student loans.
• Comprehensive Retirement Savings Program with employer matched contributions.
• Annual Wellness allowance to support employees with improving health and wellbeing.
• Personal days.
• Tuition Reimbursement.
• Working within the community and giving back.
• Expected compensation for this role ranges from $ 80,900.00 - 129,725.00 annually. Actual salary offered to successful candidates will vary based on their skills and experience.