The Royal Caribbean Group's AI & Analytics Team has an exciting career opportunity for a full-time
Senior Data Scientist reporting to the Senior Manager , Data Science
The position is onsite and based in Miami, Florida.Position Summary: We are seeking a Sr. Data Scientist to lead complex data science work from business framing through production operations, making model decisions understandable, measurable, and adopted across Royal Caribbean Group. This role emphasizes Data Science ownership of delivered business value: framing the right problem, building and validating ML/optimization/GenAI solutions, partnering on deployment, monitoring performance, and driving adoption in production. The ideal candidate combines statistical and machine learning depth with practical business judgment, strong stakeholder partnership, and the ability to convert analytical work into measurable outcomes rather than isolated prototypes.
- EssentialDutiesandResponsibilities:Problem Framing & Value: Frame high-impact business problems for senior independent model ownership and cross-functional influence into measurable data science opportunities with clear decision owners, baseline metrics, adoption paths, and expected value tied to multi-process improvements in revenue, cost, service, capacity, personalization, or operational decision quality.
- Predictive Modeling: Develop forecasting, propensity, classification, and ranking models using Python, scikit-learn, XGBoost, LightGBM, CatBoost, and Databricks feature workflows to support production decisions.
- Prescriptive Decisioning: Build recommendation, simulation, and optimization solutions using MILP, heuristics, dynamic programming, or scenario modeling to improve operational and commercial decisions.
- GenAI Solutions: Design GenAI workflows using GPT-class models, Azure AI Foundry, RAG, embeddings, prompt engineering, and evaluation routines where natural-language or agentic capabilities improve business productivity.
- Statistical Experimentation: Design and evaluate A/B tests, quasi-experiments, causal analyses, bootstrap methods, and non-parametric tests to determine whether model or process changes create measurable lift.
- Explainability & Trust: Apply SHAP, sensitivity analysis, model diagnostics, error analysis, and stakeholder-ready explanations so users understand model behavior, limits, and decision implications.
- Production Deployment: Partner with AI Engineering to deploy models and analytical applications through Databricks, Azure ML, MLflow, APIs, or containerized services while retaining accountability for business value and model behavior.
- Production Operations: Monitor accuracy, drift, bias, adoption, latency, cost, and business KPIs; trigger retraining, recalibration, or process changes when performance or value realization degrades.
- Stakeholder Partnership: Partner with business, product, operations, AI Engineering, and data engineering teams to convert model outputs into decisions, workflows, incentives, and measurable adoption.
- Qualifications, Knowledge and Skills:Education: Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Operations Research, Engineering, Economics, or a related quantitative field, or equivalent practical experience.
- Experience: Demonstrated experience appropriate to senior scope delivering ML, optimization, experimentation, or GenAI solutions that moved beyond analysis into production use or business decisioning.
- ML Tooling: Hands-on experience with Python, scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch or TensorFlow where appropriate, and model evaluation workflows for production-grade use cases.
- Optimization: Experience with MILP solvers, simulation, scenario planning, dynamic programming, heuristics, or prescriptive analytics methods applied to real business decisions.
- GenAI Platforms: Experience with Azure AI Foundry, GPT-class models, RAG, embeddings, prompt engineering, evaluation, and safe use of GenAI for decision support or workflow automation.
- Data Platforms: Advanced use of Databricks, Spark, SQL, feature pipelines, data quality checks, and reproducible analytical workflows for large-scale data science delivery.
- MLOps: Experience with MLflow, Azure ML, model registries, CI/CD, monitoring, retraining, and production handoff practices that keep models reliable after launch.
- Engineering: Strong Python engineering practices, Git workflows, testing, packaging, notebooks-to-production discipline, APIs, and collaboration with AI Engineering for deployment readiness.
- Communication: Clear communication skills with domain leaders, product owners, AI engineers, data engineers, and senior business stakeholders, including the ability to explain model logic, uncertainty, tradeoffs, risks, and recommended decisions in business terms.
Power Skills:- Action Oriented
- Collaborates Effectively
- Communicates Effectively
- Drives Results
- Situational Adaptability
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!
