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About This Role
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
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!
*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*
Nearest Major Market: Miami
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Royal Caribbean Group, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Royal Caribbean Group AI Hiring
Royal Caribbean Group has 1 open AI role right now. They're hiring across Data Scientist. Based in Miami, FL, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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