Manager, Data Science

Four Seasons Hotels and Resorts • Toronto, ON

43d • CA$85,000 - CA$125,000 • Hybrid

About The Position

The Manager, Data Science is an applied data science leader within the Data & Analytics function and part of the Technology Innovation & Data (TID) department, supporting Commercial and enterprise-wide initiatives. This role bridges business challenges and technical execution—owning problem framing, analytical approach, modeling/experimentation/measurement, and stakeholder alignment to deliver clear business outcomes (e.g., revenue growth, cost savings, improved guest experience). Reporting to the Director, Data Science & Analytics, this role will work cross-functionally with Data Engineering, Data Governance, and Data Delivery, the Manager, Data Science will apply Four Seasons’ technical standards to operationalize AI solutions from prototype to production.

Requirements

• 5+ years of working experience in data science, data wrangling, management, and/or ML engineering.
• University degree in Computer Science, Statistics, Data Science, Applied Mathematics, Engineering, or substantial coursework in relevant quantitative field.
• Applied modeling and analytics: strong foundation in statistics and ML (supervised/unsupervised), feature engineering, and model evaluation; ability to explain tradeoffs and drive business decisions.
• Experience in NLP and/or GenAI techniques for applied use cases (e.g., classification, entity extraction, semantic search) with an evaluation-first mindset.
• Data proficiency: advanced SQL and Python; strong data wrangling skills (profiling, cleansing, merging, validation) for large, messy datasets.
• Cloud & tooling (preferred): Azure and Databricks ecosystem (e.g., Databricks, Data Factory, Data Lake/Blob, Azure Function App, Azure AI Foundry), with ability to run scalable experimentation and modeling workflows.
• Production readiness (in partnership with Engineering): familiarity with CI/CD, version control (Azure DevOps/GitHub), reproducible pipelines, and monitoring principles for models and data products.
• Visualization & communication: experience using notebooks and BI tools (e.g., Power BI, Tableau, PowerPoint) to tell clear stories to technical and non-technical stakeholders.
• Experimentation and measurement: experience designing and interpreting experiments (A/B tests, holdouts, quasi-experiments) and defining success metrics for product/business outcomes.
• Proactive to understand business needs and how data will be used to drive strategic decisions and tactical action plans.
• Translates data science outputs to provide clear, actionable recommendations that support leadership decision-making.
• Tailors messaging to the audience, focusing on clarity, brevity, and relevance.
• Collaborate effectively with cross-functional colleagues, external consultants, and agencies.
• Works well under pressure and can manage multiple tasks under time constraints.
• Well organized, detail-oriented, able to multi-task in a highly iterative environment.
• Creative, performance-driven problem solver with a strong sense of ownership and discipline; passionate about leveraging digital, AI, ML, and data to drive business transformation.
• Solid foundation in Math and Statistics.
• Ability to sift through large data sets, identify patterns and know how to use that data to come to meaningful and actionable conclusions.
• Work cross-functionally in a matrix organization.
• Project Management: Organize and manage processes and expectations, and deliver according to key deadlines.

Responsibilities

• Lead Data Science in Designing Prototypes Supporting Commercial Strategy
• Own applied data science problem framing: translate business goals into clear hypotheses, success metrics, and an analytical approach (e.g., forecasting, propensity, segmentation, optimization, NLP).
• Lead the design and delivery of data science prototypes aligned to commercial strategy, ensuring measurable business impact (e.g., cost savings, revenue growth, improved guest experience).
• Define and execute measurement and experimentation plans (e.g., A/B tests, holdouts, quasi-experiments) to quantify incremental impact and guide product/business decisions.
• Plan and manage the data science / ML project pipeline by developing roadmaps, aligning business priorities, communicating tradeoffs, and coordinating dependencies with partners (e.g., Data Engineering, Data Governance, Product/Digital/Business teams).
• Identify, collect, and validate datasets; define data quality checks and partner with data stewards/engineering to ensure data is fit for purpose.
• Design, develop, and evaluate models and prototypes; select the best approach balancing accuracy, interpretability, scalability, and business constraints.
• Present insights and recommendations to non-technical and leadership audiences, driving alignment, decision-making, and adoption.
• Raise the bar on team quality: conduct peer code reviews, mentor team members, and document approaches (Confluence) to establish reusable patterns and best practices.
• As applicable, manage third parties during the prototyping phase to ensure schedules, processes, and outcomes are monitored and achieved.
• Support Machine Learning Deployments
• Develop, test, optimize, and productionize machine learning models, including their supporting data pipelines for training and predictions.
• Develop and embed automated processes for predictive model validation and QA.
• Stage deployments to enable collaborative QA and controlled releases, monitor and version control changes.
• Monitor health and performance of production ML products and relative performance of competing models.
• Promote, contribute, and define coding guidelines to raise the bar for code quality.
• Develop and Support Generative AI Solutions
• Develop and optimize Gen AI workflows using LLMs, RAG, vector search, and AI orchestration frameworks.
• Partner with engineering, integrations, and platform teams to support deployment of LLM-based applications, including retrieval pipelines, and prompt orchestration and governance across development and production environments.
• Design evaluation suites and QA approaches for GenAI solutions (e.g., test sets, automated checks, human review), including responsible AI guardrails appropriate to the use case.
• Contribute to reusable AI components, prompt engineering standards, and AI solution design best practices.
• Partnerships and Following Four Seasons Standards
• Follows technical direction set by Enterprise Data Architect and Data Engineering team.
• Manage data science projects from conception to delivery through the organized intake process, track tasks and progress using Jira and Monday.com, and adhere to governance standards and successful delivery methodologies.
• Learn and implement Four Seasons technical standards, procedures and processes including FS DevOps.
• Test and verify the completion of work done by Data Engineering to ensure compliance with the original intent of prototype and long-term requirements.

Benefits

• Dedicated to perfecting the travel experience through continual innovation and the highest standards of hospitality, Four Seasons can offer what many hospitality professionals dream of -the opportunity to build a life-long career with global potential and a real sense of pride in work well done.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager