Manager, Data Science

Royal Caribbean Cruises Ltd•Miami, FL

19h•Onsite

About The Position

The Manager, Data Science leads a data science team responsible for building the intelligence layer that helps Marketing determine which customers matter, who is persuadable, what experience or message to present, and whether interventions create incremental value. The portfolio spans customer lifetime value (CLV), customer targeting, loyalty, synthetic-personality testing, and media measurement. The role calls for expertise in predictive modeling, causal measurement, experimentation, segmentation, and GenAI, along with the ability to turn analytical outputs into activated audiences, measurable campaigns, and durable customer intelligence. This full-time role is part of Royal Caribbean Group’s Digital Team, reports to the Sr. Manager, Data Science, and is based onsite in Miami, Florida. It 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, with judgment to challenge assumptions and explain tradeoffs.
Ability to translate business strategy into sequenced analytical roadmaps, model portfolios, adoption plans, and value-tracking routines aligned to data scientists, product owners, AI engineering, data engineering, and business stakeholders.
Working knowledge of value sizing, ROI framing, experimentation economics, cloud/model operating costs, and investment tradeoffs.
Clear communication skills 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 levels of analytical maturity.
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 Marketing, customer targeting, CLV, Loyalty, and experimentation, keeping technical coaching and business translation close to delivery teams.
Own delivery of CLV assets, propensity and audience models, Loyalty simulation, synthetic-personality testing, and media measurement, from analytical design through activation, adoption, and measured outcomes.
Provide technical direction across CLV, propensity and uplift modeling, segmentation, recommender systems, causal inference, brand-lift testing, simulation, synthetic-customer evaluation, and model monitoring.
Prioritize Marketing work based on decision value, activation readiness, data and consent readiness, measurement feasibility, model risk, and reuse across Royal Caribbean International, Celebrity Cruises, and Silversea.
Partner with Marketing, E-Commerce, Loyalty, Digital, Consumer Insights, Media Network, Product, AI Engineering, Data Engineering, and brand teams to translate growth questions into analytical roadmaps and activation plans.
Drive adoption through audience activation, campaign operating routines, Loyalty scenario planning, test-and-learn standards, stakeholder training, and clear accountability for using model outputs.
Ensure customer models and synthetic testing follow Responsible AI, privacy, consent, explainability, validation, auditability, and ethical-use expectations, including controls for protected data and marketing eligibility.
Forecast capacity across model refreshes, campaign scoring, experiment design, CLV data production, Loyalty simulations, media studies, and production support.
Build team depth across customer analytics, causal measurement, experimentation, segmentation, CLV, recommendations, and GenAI-based research while developing strong partnerships with Marketing and Digital leaders.