R
Royal Caribbean Groupvia Indeed
Data Scientist
Miami, FL, USPosted 2mo ago
Data ScientistMid LevelFull-time#python#pytorch#tensorflow#huggingface#nlp#docker#azure#spark#mlflow
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About the 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 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 (eg 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 (eg
- 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 (eg 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
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
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