Manager, Data Science

Journey with us! Combine your career goals and sense of adventure by joining our exciting team of employees. Royal Caribbean Group is pleased to offer a competitive compensation and benefits package, and excellent career development opportunities, each offering unique ways to explore the world.

 

We are proud to be the vacation-industry leader with global brands — including Royal Caribbean International, Celebrity Cruises and Silversea Cruises — the most innovative fleet and private destinations, and the best people. Together, we are dedicated to turning the vacation of a lifetime into a lifetime of vacations for our guests.

The Royal Caribbean Group’s AI and Analytics  Team has an exciting career opportunity for a full time Manager, Data Science reporting to the Sr. Manager, Data Science.

 

The position is onsite and based in Miami, Florida.

 

The position is also not eligible for work authorization sponsorship.

 

Position Summary:

To lead a data science team accountable for applying AI and decision science to Royal Caribbean Group’s highest-stakes operational and capital decisions across Newbuild, Global Marine Operations, and Deployment. The portfolio spans the Newbuild Enterprise Observatory, asset and vessel intelligence, decarbonization and energy analytics, safety and risk decision support, and deployment optimization. The role requires predictive modeling, optimization, digital-twin, GenAI, and knowledge-system depth, combined with the judgment to manage long-horizon decisions, operational trade-offs, and adoption across complex technical domains.

• People Leadership: Lead, coach, and develop data science talent across Newbuild, GMO, Safety, Energy, and Deployment, placing technical guidance and domain translation close to the teams while enabling directors to focus on fewer, higher-value priorities.

• Delivery Accountability: Own reliable delivery of knowledge, predictive, optimization, Digital Twin, safety, and deployment initiatives from problem framing through operational adoption, monitoring, and measurable decision improvement.

• Technical Oversight: Provide technical direction across GenAI knowledge systems, forecasting, predictive maintenance and reliability, Digital Twins, emissions and fuel models, risk analytics, simulation, and constrained optimization.

• Portfolio Management: Prioritize work by decision criticality, capital and operating exposure, safety relevance, adoption readiness, data maturity, integration dependencies, and production feasibility across long-duration programs.

• Stakeholder Management: Partner with Newbuild, GMO, Decarbonization, Energy Management, Safety, Risk, Deployment, Revenue Planning, Finance, IT, AI Engineering, Data Engineering, architects, and operational leaders to define decisions, trade-offs, and ownership.

• Change Management: Move solutions beyond repositories and dashboards by embedding knowledge, forecasts, and optimization outputs into recurring Newbuild, fleet, energy, safety, and deployment decisions with clear operating routines and accountability.

• Governance & Risk: Ensure assets follow Responsible AI, validation, traceability, explainability, auditability, cybersecurity, privacy, safety, and model-risk expectations appropriate to capital, regulatory, and vessel-operating decisions.

• Resource Planning: Forecast capacity across long-horizon Newbuild work, production marine analytics, energy forecasting, safety support, and deployment optimization so urgent operational needs do not crowd out strategic asset development.

• Talent Strategy: Build depth across optimization, Digital Twins, energy and emissions analytics, reliability, safety and risk modeling, GenAI knowledge systems, and production operations while developing credible technical partners to business leaders.

Qualifications:

• Experience: Demonstrated experience appropriate to manager scope delivering ML, optimization, experimentation, or GenAI solutions that moved beyond analysis into production use or business decisioning. Relevant experience in the assigned portfolio domain is strongly preferred.

• Technical Credibility: Strong grounding in statistics, machine learning, optimization, GenAI methods, evaluation, and production model lifecycle practices, with the judgment to challenge assumptions and explain tradeoffs.

• Strategic Leadership: 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.

• Financial Acumen: Working knowledge of value sizing, ROI framing, experimentation economics, cloud/model operating costs, and investment tradeoffs.

• Communication: Clear communication skills with data scientists, product owners, AI engineering, data engineering, and business stakeholders, including the ability to explain model logic, uncertainty, tradeoffs, risks, and recommended decisions in business terms.

• Change Leadership: Track record of driving adoption of analytical products, experimentation practices, model governance, and data-driven operating routines across teams with different analytical maturity levels.

• Governance: Knowledge of Responsible AI, privacy, explainability, validation, auditability, and ethical use of data, with experience embedding governance into data science workflows.

• Vendor Management: 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.

• Organizational Design: Understanding of how to scale data science teams, intake models, value governance, production support, stakeholder engagement, and career paths as analytical demand grows.

HOW HAS THE JOB CHANGED/GROWN

 

The role has expanded from supporting individual marine or analytical use cases into leading an integrated operational and capital decision portfolio. The manager must connect institutional knowledge, Digital Twins, predictive analytics, safety intelligence, and optimization while driving adoption in recurring decisions rather than creating standalone repositories or prototypes.

 

INTERNAL/ EXTERNAL RELATIONSHIPS

  Internal – Newbuild, GMO, Decarbonization, Energy Management, Safety, Risk, Deployment, Revenue Planning, Finance, IT, AI Engineering, and Data Engineering: Translate high-stakes capital and operational questions into knowledge, predictive, simulation, and optimization roadmaps.

 

• Internal – Architects, fleet leaders, operators, and decision owners: Embed outputs into recurring decisions, establish traceability and feedback loops, and measure decision speed, quality, and reuse.

 

• External – Shipyard, engineering, data, model, Digital Twin, optimization, and consulting partners: Evaluate external inputs based on technical validity, business value, integration effort, intellectual-property leverage, risk, and maintainability.

We know there's a lot to consider. As you go through the application process, our recruiters will be glad to provide guidance, and more relevant details to answer any additional questions. Thank you again for your interest in Royal Caribbean Group. We'll hope to see you onboard soon!

 

It is the policy of the Company to ensure equal employment and promotion opportunity to qualified candidates without discrimination or harassment on the basis of race, color, religion, sex, age, national origin, disability, sexual orientation, sexuality, gender identity or expression, marital status, or any other characteristic protected by law. Royal Caribbean Group and each of its subsidiaries prohibit and will not tolerate discrimination or harassment.