Associate Data Scientist

Data Science Analyst

Hybrid Role

Compensation: $40 - $42

Royal Caribbean Group is seeking a Data Science Analyst to support end-to-end data science delivery by translating business questions into actionable analyses, model components, and production-ready outputs. This role emphasizes ownership of delivered business value—framing the right problems, building and validating machine learning, optimization, and 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.

The position may require regular domestic or international travel and work in both office and shipboard environments.

• Frame high-impact business problems for analytics and supervised model delivery, defining clear decision owners, baseline metrics, adoption paths, and expected value tied to decision support, operational visibility, and automation.
• Develop and validate predictive models (forecasting, propensity, classification, ranking) using Python, scikit-learn, XGBoost, LightGBM, Cat Boost, and Databricks feature workflows to support production decisions.
• Build prescriptive analytics solutions, including recommendation, simulation, and optimization models using MILP, heuristics, dynamic programming, or scenario modeling to improve operational and commercial outcomes.
• Design and implement GenAI workflows leveraging GPT-class models, Azure AI Foundry, RAG, embeddings, prompt engineering, and evaluation routines to enhance business productivity and workflow automation.
• Design and evaluate statistical experiments (A/B tests, quasi-experiments, causal analyses, bootstrap methods, non-parametric tests) to measure the impact of model or process changes and determine measurable lift.
• Apply explainability techniques (e.g., SHAP, sensitivity analysis, model diagnostics, error analysis) to ensure model transparency, stakeholder understanding, and trust in model-driven decisions.
• Partner with AI Engineering to deploy models and analytical applications through Databricks, Azure ML, MLflow, APIs, or containerized services, retaining accountability for business value and model behavior.
• Monitor production models for accuracy, drift, bias, adoption, latency, cost, and business KPIs; trigger retraining, recalibration, or process changes as needed to maintain performance and value realization.
• Collaborate with business, product, operations, AI Engineering, and data engineering teams to convert model outputs into decisions, workflows, incentives, and measurable adoption.

• 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 delivering ML, optimization, experimentation, or GenAI solutions in production or business decisioning contexts, with a focus on measurable business impact.
• Hands-on experience with Python, scikit-learn, XGBoost, LightGBM, Cat Boost, PyTorch or Tensor Flow, 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…