Manager, Data Science

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. 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. 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. 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.

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