Manager, Data Science

Royal Caribbean Cruises Ltd•Miami, FL

1d•Onsite

About The Position

The Manager, Data Science leads a data science team responsible for extending AI-driven commercial decisioning across Royal Caribbean Group’s Onboard Revenue and Casino areas. The role connects pricing, targeted offers, personalization, product recommendations, guest value, and casino offer decisioning to measurable production outcomes. It combines statistical, machine learning, optimization, and experimentation expertise with commercial judgment to translate guest and product signals into decisions about price, offer, product, guest, and timing. The manager leads and develops data science talent, owns delivery of commercial decisioning roadmaps from problem framing through production adoption and ongoing operations, and provides technical direction across modeling and decisioning methods. The role prioritizes a portfolio of commercial initiatives, collaborates with business and technical stakeholders, and embeds recommendations into operating processes. It also oversees governance and risk, plans resources and capacity, and builds team capabilities as demand grows. This is a full-time, onsite position based in Miami, Florida, reporting to the Sr. Manager, Data Science. The position is not eligible for work authorization sponsorship.

Requirements

Demonstrated experience appropriate to manager scope delivering ML, optimization, experimentation, or GenAI solutions that progressed beyond analysis into production use or business decisioning.
Strong grounding in statistics, machine learning, optimization, GenAI methods, evaluation, and production model lifecycle practices.
Ability to translate business strategy into sequenced analytical roadmaps, model portfolios, adoption plans, and value-tracking routines aligned with data scientists, product owners, AI engineering, data engineering, and business stakeholders.
Working knowledge of value sizing, ROI framing, experimentation economics, cloud and model operating costs, and investment tradeoffs.
Clear communication with technical and business stakeholders, including the ability to explain model logic, uncertainty, tradeoffs, risks, and recommended decisions in business terms.
Track record of driving adoption of analytical products, experimentation practices, model governance, and data-driven operating routines across teams with different analytical maturity levels.
Knowledge of Responsible AI, privacy, explainability, validation, auditability, and ethical use of data, with experience embedding governance into data science workflows.
Ability to evaluate external data, model providers, optimization tools, GenAI services, and consulting support based on business value, accuracy, integration effort, risk, and long-term leverage.
Understanding of how to scale data science teams, intake models, value governance, production support, stakeholder engagement, and career paths as analytical demand grows.

Nice To Haves

Relevant experience in the assigned portfolio domain is strongly preferred.

Responsibilities

Lead, coach, and develop data science talent across Onboard Revenue and Casino, supporting technical coaching, business translation, and day-to-day execution.
Own reliable delivery of pricing, targeted-offer, personalization, recommendation, and casino decisioning roadmaps from problem framing through production adoption, controlled measurement, and ongoing operations.
Provide technical direction across price-response modeling, demand forecasting, propensity and uplift modeling, recommender systems, segmentation, constrained optimization, experimentation, and real-time decisioning.
Prioritize work across PCP pricing, promotions, Targeted Offers, MyCruise personalization, shipboard offers, and Casino based on incremental revenue potential, adoption readiness, dilution risk, data readiness, and production feasibility.
Partner with Onboard Revenue, Casino, Digital, E-Commerce, Marketing, Product, AI Engineering, Data Engineering, and brand leaders to define decision owners, business guardrails, measurement plans, and operating workflows.
Embed recommendations into analyst, merchandising, campaign, shipboard, and casino operating processes through transparent logic, workflow integration, training, and control-group measurement.
Ensure guest-level targeting, offer, pricing, and reward decisions meet Responsible AI, privacy, fairness, explainability, legal, validation, and model-risk expectations, including controls against promotional dilution and conflicting guest contacts.
Forecast capacity across model development, experimentation, production support, campaign execution, pricing coverage, and stakeholder adoption.
Build team depth across pricing science, causal measurement, propensity and uplift modeling, recommendations, optimization, and production decision systems, while developing business-facing leaders.
Align business guardrails, test/control measurement, value tracking, and adoption accountability with brand and Finance stakeholders.
Evaluate external data, model, optimization, and consulting capabilities based on incremental value, accuracy, integration effort, responsible use, risk, and long-term leverage.