Data Scientist, Revenue Management Systems

GROW YOUR CAREER WITH US
At Norwegian Cruise Line Holdings (NCLH), we know our future success depends on our ability to attract and retain the very best talent. Our brands deliver vacations of a lifetime with innovative product offerings, a high level of service and unique guest experiences aboard each vessel and we’re continually seeking applicants who are passionate about hospitality and committed to being their personal best. As you learn more about our company, we think you will agree that there is no better time than now to become a member of the NCLH family!

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JOB SUMMARY

The Data Scientist within Revenue Management Systems will be responsible for building, scaling, and validating the predictive models, forecasting logic, and advanced analytics frameworks that power Norwegian Cruise Line's dynamic pricing and inventory management decisions. This individual contributor role bridges statistical engineering with commercial operations—translating massive datasets, historical transaction curves, and high-intent guest behavioral data into clear, high-yield revenue recommendations.

Working closely with senior leadership, data engineers, and revenue managers, the Data Scientist will write production-grade SQL and Python code to design scalable forecasting algorithms and price elasticity frameworks. The ideal candidate thrives on extracting value from complex database environments (e.g., Snowflake) and converting technical model performance into clear business insights.

POSITION RESPONSIBILITIES

• Predictive Model Construction: Design, write, and maintain scalable algorithms and machine learning models for booking curves, price elasticity, cancellation rates, and passenger cabin upgrades (e.g., Plusgrade).
• Data Pipeline & ETL Engineering: Query, clean, aggregate, and manipulate large-scale datasets from disparate corporate ecosystems using Snowflake, SQL, and Python to ensure reliable inputs for quantitative modeling.
• RMS Calibration & Evaluation: Monitor, fine-tune, and analyze baseline calibration thresholds within enterprise Revenue Management Systems (RMS) to reduce forecast variances and automate routine algorithmic workflows.
• A/B Testing & Attribution: Formulate rigorous tracking and measurement frameworks, using statistical methodologies and panel data techniques to validate the exact revenue impacts of tactical promotions and digital pricing optimizations.
• Business Intelligence Support: Architect, deploy, and maintain insightful data visualization dashboards in Power BI or Tableau to translate modeling results and performance metrics into clear stories for commercial stakeholders.
• Cross-Functional Collaboration: Partner closely with IT and data engineering teams to operationalize prototypes into robust production systems, while communicating quantitative logic clearly to non-technical business partners.

QUALIFICATIONS

DEGREE TYPE:

Bachelor's Degree

FIELD(S) OF STUDY:

Business Administration, Hospitality Management, Finance, Marketing, or a related field

EXPERIENCE

• Bachelor’s degree in Data Science, Statistics, Mathematics, Operations Research, Economics, Computer Science, or a heavily quantitative discipline is required.
• Master’s degree (MS) in a quantitative field is a plus but not required with equivalent professional experience.
• 3–5 years of progressive professional experience working as a data scientist, quantitative analyst, or modeler—ideally building systems that influence business pricing, sales, or financial forecasting.
• Hands-on experience manipulating, structuring, and scrubbing large, raw datasets within enterprise cloud-based or local architectures.
• Prior experience working in dynamic commercial fields with highly perishable inventory (e.g., cruise lines, aviation, hospitality, logistics, or consumer tech) is highly preferred.
• Experience in a role that required presenting complex outputs in clear and concise way that influenced leadership.

COMPETENCIES & SKILLS

• Advanced Querying & Coding: Strong mastery of programming languages required for statistical computing, data architecture, and ETL execution, specifically Python and advanced SQL.
• Cloud Environments: Hands-on familiarity extracting and joining complex relational data within cloud data platforms like Snowflake, Databricks, or cloud equivalents.
• Data Visualization: Demonstrated capability building, hosting, and automating rep