Data Scientist, Revenue Management Systems

Prestige Cruise Services LLC; Miami (FL); On-site; USD 120,000 - 180,000; Full time

Job summary

Norwegian Cruise Line Holdings (NCLH) is seeking an experienced Data Scientist to build, validate, and scale predictive models powering dynamic pricing and inventory decisions. You will translate complex datasets into actionable revenue recommendations and collaborate with data engineers and revenue managers.

The role involves production-grade SQL and Python, forecasting algorithms, and dashboard development to communicate insights to commercial stakeholders.

Qualifications

• Bachelor's or higher in a quantitative discipline or related field.
• Hands-on data science experience with large datasets and production-grade code.
• Experience in pricing, forecasting, or revenue management is a plus.
• Strong communication of complex analyses to non-technical stakeholders.

Responsibilities

• Design and implement predictive models for booking curves and price elasticity.
• Develop data pipelines and ETL processes for reliable inputs to models.
• Calibrate RMS thresholds and monitor forecast variances in production systems.
• Build dashboards in Power BI/Tableau to visualize model results for leadership.
• Collaborate with IT and data engineers to productionize analytical prototypes.

Skills

Python

SQL

Data Visualization

Forecasting

Cloud Platforms

A/B Testing

Education

Bachelor's degree in quantitative field

Master's degree in quantitative field

Tools

Snowflake

Databricks

Power BI

Tableau

Python

SQL

Job description

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!

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

• 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 reports or data visualization dashboards in Power BI or Tableau.
• Statistical & ML Frameworks: Knowledge of regression analysis, time-series forecasting ARIMA/Prophet, optimization algorithms, and common machine learning packages (scikit-learn, XGBoost, etc.).
• Analytical Curiosity: A natural drive to unearth trends in chaotic, complex transaction data and translate those findings into logical business mec