Senior Data Scientist

Royal Caribbean Cruises Ltd•Miami, FL

38d•Onsite

About The Position

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. 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.

Requirements

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.
Demonstrated experience appropriate to senior scope delivering ML, optimization, experimentation, or GenAI solutions that moved beyond analysis into production use or business decisioning.
Hands-on experience with Python, scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch or TensorFlow where appropriate, and model evaluation workflows for production-grade use cases.
Experience with MILP solvers, simulation, scenario planning, dynamic programming, heuristics, or prescriptive analytics methods applied to real business decisions.
Experience with Azure AI Foundry, GPT-class models, RAG, embeddings, prompt engineering, evaluation, and safe use of GenAI for decision support or workflow automation.
Advanced use of Databricks, Spark, SQL, feature pipelines, data quality checks, and reproducible analytical workflows for large-scale data science delivery.
Experience with MLflow, Azure ML, model registries, CI/CD, monitoring, retraining, and production handoff practices that keep models reliable after launch.
Strong Python engineering practices, Git workflows, testing, packaging, notebooks-to-production discipline, APIs, and collaboration with AI Engineering for deployment readiness.
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.

Nice To Haves

Action Oriented
Collaborates Effectively
Communicates Effectively
Drives Results
Situational Adaptability

Responsibilities

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.
Develop forecasting, propensity, classification, and ranking models using Python, scikit-learn, XGBoost, LightGBM, CatBoost, and Databricks feature workflows to support production decisions.
Build recommendation, simulation, and optimization solutions using MILP, heuristics, dynamic programming, or scenario modeling to improve operational and commercial decisions.
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.
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.
Apply SHAP, sensitivity analysis, model diagnostics, error analysis, and stakeholder-ready explanations so users understand model behavior, limits, and decision implications.
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.
Monitor accuracy, drift, bias, adoption, latency, cost, and business KPIs; trigger retraining, recalibration, or process changes when performance or value realization degrades.
Partner with business, product, operations, AI Engineering, and data engineering teams to convert model outputs into decisions, workflows, incentives, and measurable adoption.