Analyst, Data Science

Royal Caribbean Cruises Ltd•Miami, FL

5d•Onsite

About The Position

The Analyst, Data Science is a member of Royal Caribbean Group’s Revenue Strategy team and supports the team’s annual revenue target by helping develop, operate, and maintain machine-learning models and automation processes for pricing, inventory, and target setting. The role identifies opportunities to optimize and automate Revenue Management initiatives and conducts statistical testing and experimentation to help define business rules and best practices. The analyst applies technical and statistical knowledge to business problems while developing the skills to take increasing ownership of models and projects. The position offers hands-on experience with pricing, inventory, and target-setting models, cloud technologies, and mentorship from experienced business leaders and data scientists. This is a full-time, onsite position based in Miami, Florida, reporting to the Data Engineering Senior Director.

Requirements

Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, or a related quantitative field.
1–3 years of experience in a data science, data, or cloud engineering role, or comparable skills demonstrated through internships or academic projects.
Experience with relational SQL and NoSQL databases.
Proficiency in a scripting language for data analysis and modeling, such as Python or R.
Strong analytical skills, including a foundation in statistics and machine learning.
Exposure to processes supporting data transformation, data structures, metadata, dependency management, and workload management.
Ability to communicate technical results clearly to technical and non-technical stakeholders.
Ability to collaborate with technical peers.
Ability to work independently with guidance and as part of a team.
Creativity in analytical and problem-solving work, and willingness to learn and act on feedback.
Developing knowledge of methodologies, tools, best practices, and processes within the area of responsibility.

Responsibilities

Assemble, clean, and prepare large, complex data sets to meet business requirements and support model development and analysis.
Identify and implement process improvements, including automating manual processes, optimizing data delivery, and improving data-pipeline scalability.
Build and maintain data pipelines to extract, transform, and load data from a wide variety of sources using SQL, Python, and cloud big-data technologies.
Build analytics and visualization tools that turn model outputs into actionable insights on business performance metrics such as customer acquisition and operational efficiency.
Create and support reporting, including Power BI reporting, to track initiatives and KPIs.
Work with Revenue Management, Marketing, Data Science, and Data Engineering stakeholders to address data-related questions and communicate results clearly.
Support stakeholder communication, training, and adoption to facilitate change management for new models and automation.
Support the development, testing, and validation of machine-learning models and business rules for pricing, inventory, and target setting, using structured experimentation under the guidance of senior data scientists.
Collaborate with data science and data engineering colleagues, seek feedback, and improve the accuracy and functionality of data systems and models.
Look for opportunities to automate work and provide more timely, accurate, and complete business analytics.