Data Scientist supporting cross-functional AI/ML initiatives at Royal Caribbean Group. Designing, developing, and delivering analytical solutions with a focus on machine learning.
Responsibilities
Support cross-functional AI/ML initiatives across Royal Caribbean Group.
Contribute to the design, development, and delivery of robust 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.
Partner with data engineers to define dataset requirements, validate data quality, and ensure pipeline reliability.
Design A/B tests, multivariate tests, and uplift experiments aligned with statistical rigor.
Create clear, actionable presentations, readouts, and memos that translate analytics into business impact.
Maintain fluency with emerging ML algorithms, cloud tooling, vector databases, responsible AI guidelines, and Azure ecosystem updates.
Requirements
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
Travel Industry eCommerce experience preferred
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.
Soft Skills: Strong analytical and critical-thinking capabilities with structured problem-solving ability.
Clear, concise communication across technical and non-technical audiences.
Ability to manage multiple priorities, adapt to evolving requirements, and maintain high attention to detail.
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