Royal Caribbean Group is a company that offers exciting career opportunities and a competitive benefits package. The Data Scientist plays a critical role in supporting cross-functional AI/ML initiatives, contributing to the design, development, and delivery of production-grade models and analytical solutions.
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)
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)
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
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
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
Qualification
Required
Bachelor's Degree in business or a technology-related area of study preferred
2+ years of experience as a Product Owner or Product Manager in eCommerce or 4+ years of proven working experience in digital marketing
Experience with agile software development processes, requirements management, and project management using JIRA/Confluence or similar Agile collaboration tools
Experience in working with CRM teams to execute a complex, highly segmented, and personalized email communication strategy
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)
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)
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
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
Preferred
Travel Industry eCommerce experience preferred
Exposure to deep-learning frameworks (PyTorch, TensorFlow) and NLP libraries (spaCy, Hugging Face) is a plus
Benefit
Competitive compensation and benefits package
Excellent career development opportunities
Royal Caribbean Group is a cruise vacation company with a global fleet of 63 ships traveling around the world.
