Data Scientist

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The Royal Caribbean Group’s AI & Analytics Team has an exciting career opportunity for a full-time Data Scientist reporting to the Senior Manager Data Scientist.

The position is onsite and based in Miami, Florida.

PositionSummary:

The Data Scientist plays a critical role in supporting cross-functional AI/ML initiatives across Royal Caribbean Group. This position contributes to the design, development, and delivery of robust production-grade models and analytical solutions. The role blends statistical analysis, machine learning engineering, experimentation, and business partnership to drive measurable value.

Candidates should have strong foundations in ML, data wrangling, statistics, and software-centric approaches to analytics. The role requires curiosity, disciplined problem solving, and the ability to translate ambiguous business questions into structured analytical tasks. Data Scientists are expected to interface with business stakeholders, data engineers, product owners, and senior DS talent while demonstrating ownership of deliverables and continuous professional growth.

• EssentialDutiesandResponsibilities:
  
  
• Machine Learning & Analytics Execution
• Perform deep exploratory data analysis to identify patterns, anomalies, data quality issues, and signal strength.
• Conduct end-to-end feature engineering including feature selection, encoding, scaling, transformation, leakage prevention, and feature importance evaluations.
• Build and tune predictive models using regression, classification, clustering, ensemble methods, and time-series forecasting.
• Apply model validation techniques such as cross-validation, bootstrapping, hyperparameter search, and error analysis.
• Implement explainability tools (e.g., SHAP, LIME, partial dependence plots) to support interpretability and trust.
• Ensure ML artifacts follow reproducibility, documentation, and version control standards (via MLFlow / GitHub)
• 
  
  Collaboration & Delivery
• Partner with data engineers to define dataset requirements, validate data quality, and ensure pipeline reliability.
• Participate in developing model training, inference, scoring, and monitoring pipelines using Azure ML and Databricks.
• Follow MLOps best practices including Git-based versioning, CI/CD for model code, experiment tracking (MLflow), and model lineage documentation.
• Work closely with product managers and business partners to refine requirements, align on success metrics, and operationalize analytical outputs.
• Contribute to solution design reviews, architectural discussions, and integration planning.
• Support the design and deployment of dashboards, APIs, and reporting interfaces used by downstream teams. This includes Optimization Engines, NLP/Embeddings, Generative AI Agents, and front-end user applications (e.g. Container Apps and experience with tools like Streamlit/Dash/Flask/Fast API).
• Experimentation & Validation
• Design A/B tests, multivariate tests, and uplift experiments aligned with statistical rigor.
• Conduct power analysis, define sample sizes, and ensure proper randomization and control matching.
• Utilize quasi-experimental methods (e.g., difference-in-differences, synthetic controls, propensity scoring) when randomized tests are not feasible.
• Evaluate experiment outcomes through causal inference, significance testing, lift calculations, and behavioral segmentation.
• Diagnose experiment failures, identify bias risks, and refine experiment protocols.
• Contribute to reusable experiment templates, calculators, documentation, and internal best-practice playbooks.
• Collaborate with cross-functional teams to validate real-world model performance and align on adjustments.
• Communication
• Create clear, actionable presentations, readouts, and memos that translate analytics into business impact.
• Build visualizations using tools such as matplotlib, seaborn, Plotly, Power BI, or equivalent.
• Deliver model walkthroughs and technical deep dives with clarity appropriate for mixed audiences.
• Document models, code, experiments, tuning decisions, and data sources to ensure reproducibility and maintainability.
• Communicate status, risks, and recommendations proactively to project leaders.
• Continuous Learning & Improvement
• Maintain fluency with emerging ML algorithms, cloud tooling, vector databases, responsible AI guidelines, and Azure ecosystem updates.
• Participate in code reviews, pair programming, and DS guild or knowledge-sharing sessions.
• Adopt modern development practices such as modular coding, unit testing, and pipeline automation.
• Seek feedback from senior DS, engineering, and stakeholders to drive skill progression.
• Explore new libraries and techniques and contribute learnings back to the team.

Qualifications, Knowledge and Skills:

Education & Experience

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, or related field.
• 2–4 years of hands-on experience designing, building, and deploying ML models in a business environment.
• Experience working with cloud data platforms or ML infrastructure (Azure preferred).

Technical Skills

• Proficiency in Python and ML libraries such as scikit-learn, XGBoost, LightGBM, pandas, NumPy, and statsmodels.
• Solid SQL skills and familiarity with distributed data tools (Spark, Databricks).
• Exposure to deep-learning frameworks (PyTorch, TensorFlow) and NLP libraries (spaCy, Hugging Face) is a plus.
• Understanding of classical statistics: hypothesis testing, confidence intervals, regression diagnostics, ANOVA, probability theory.
• Familiarity with containerized environments (Docker), CI/CD workflows, and observability concepts.
• Knowledge of responsible AI concepts including bias detection, fairness evaluation, and privacy considerations.
• Understanding of operational constraints when deploying models, including latency, scalability, and integration considerations.
  
  

Soft Skills

• Strong analytical and critical-thinking capabilities with structured problem-solving ability.
• Clear, concise communication across technical and non-technical audiences.
• Ability to work in a cross-functional environment and contribute to collaborative team dynamics.
• Ability to manage multiple priorities, adapt to evolving requirements, and maintain high attention to detail.
• A proactive mindset, curiosity, and willingness to experiment and learn.

Power Skills:

• Action Oriented
• Collaborates Effectively
• Communicates Effectively
• Drives Results
• Situational Adaptability