Senior Analyst, Onboard Revenue; CCL

This position is classified as “in-office.” As an in-office role, it requires employees to work from a designated Carnival office in South Florida Tuesday through Thursday each week. Employees may work from their homes on Mondays and Fridays. Candidates must be located in (or willing to relocate to) the Miami/Ft. Lauderdale area.

The Onboard Revenue Senior Analyst is responsible for leveraging data analytics and advanced modeling techniques to drive revenue optimization and enhance the guest experience across onboard operations. It involves analyzing complex datasets, developing predictive models, and maintaining robust data infrastructure to support strategic decision-making. The Onboard Revenue Data Scientist will work closely with revenue management, onboard operations, marketing, finance, and IT teams to uncover insights, streamline processes, and implement data-driven solutions that align with business goals.

The ideal candidate will have strong analytical skills, experience with machine learning and data visualization, and a collaborative mindset to thrive in a cross‑functional environment.

• Analyze complex datasets from various onboard revenue streams to uncover trends, patterns, and opportunities for revenue growth. Develop comprehensive reports and dashboards that deliver actionable insights to revenue managers, operations teams, and senior leadership. Through exploratory data analysis, the role examines guest behavior, spending habits, and service utilization across different cruise itineraries and demographics. Statistical analysis is used to evaluate the effectiveness of pricing strategies, promotional campaigns, and service offerings on both guest satisfaction and revenue performance.
• Maintain and enhance data quality across multiple revenue tracking systems, ensuring accuracy and consistency in all reporting efforts. Develop and implement data validation processes to detect and resolve integrity issues, as well as documenting data sources, definitions, and analytical methodologies. Collaboration with IT teams is essential to optimize data collection processes and improve accessibility. Additionally, the role involves designing and implementing automated data pipelines to streamline reporting and analytical workflows, supporting a more efficient and scalable data infrastructure.
• Research and prototype machine learning models aimed at improving demand forecasting, dynamic pricing, and guest behavior prediction. The development of recommendation systems is a key focus, enhancing the guest experience and boosting onboard spending through personalized service suggestions. Predictive models are also implemented to optimize inventory management across retail, dining, and spa services. Explore advanced analytics techniques such as customer segmentation, lifetime value modeling, and churn prediction.
  
  Stay informed on emerging AI technologies and evaluate their potential applications within cruise revenue operations.
• Collaborate with various departments to ensure data-driven decision-making across the organization. This includes partnering with revenue managers to align analytical efforts with business objectives and working closely with onboard operations teams to understand guest journey touchpoints and identify areas for optimization. Support marketing teams in evaluating the effectiveness of promotional campaigns and guest engagement strategies, and assist finance teams with revenue forecasting and performance analysis.
  
  Participation in cross-departmental projects is essential to foster a culture of data-informed strategy and continuous improvement.
• Perform other duties as assigned.

• Bachelor’s degree in data science, Statistics, Mathematics, Computer Science, Economics, Engineering or related quantitative field.
• 6+ years of experience with SQL for database querying and data manipulation.
• Familiarity with data visualization tools such as Tableau, Power BI, or similar platforms.
• Understanding of statistical concepts, including hypothesis testing, regression analysis, and experimental design.
• Basic knowledge of machine learning algorithms and their applications.
• Demonst…