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UG :- - Not Required
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No of position :- ( 1 )
Post :- 29th Jan 2026
POSITION SUMMARY:
Royal Caribbean is seeking a seasoned, experienced and inquisitive Data Scientist that will collaborate with the centralized Data Science team on delivering high-value AI projects across diverse business areas (e.g. Revenue Management, Digital, E-Commerce & Marketing, Supply Chain, and Marine Operations). AI solutions will create measurable business impact and revenue growth by combining business domain knowledge with new, cutting-edge technologies in the field of Artificial Intelligence, Machine Learning, & Big Data. The preferred candidate is expected to work independently to develop, productionize, and maintain AI capabilities within the Cloud that will continue to keep Royal Caribbean Cruise Lines ahead of its competitors in this space.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
• A successful candidate will possess a strong background, either professionally or academically, in statistics, mathematics, artificial intelligence, research methods, experiment design, developing novel solutions beyond the use of well-established methods, and taking new technologies to a prototype state that can be expanded into a robust product.
• The candidate should demonstrate a progressive maturation of prior research projects, publications, patents or similar capabilities in the field of applied research.
• This individual will be responsible for scripting/programming, mentorship, stakeholder presentation, and collaboration with other team members to execute a research experiment within the framework of implementing, utilizing and expanding R&D capabilities within the broader Data Science team.
Major responsibilities include:
• Research and apply emerging forms of statistical, machine learning (neural networks / deep networks, ensemble methods, natural language processing, computer vision) and engineering methods to Royal Caribbean Cruise Lines, data science and AI ecosystem.
• Must be comfortable doing so with little or no documentation or reliance on research articles and papers.
• Execute these objectives with respect to both purely exploratory endeavors as well as established products requiring novel ingenuity to meet known stakeholder objectives.
• Manipulates high-volume, high-dimensionality data from varying sources to highlight patterns, anomalies, relationships and trends.
• Collaborate with Digital, IT, Business stakeholders to design and implement R&D prototypes into broader automated systems and establish a Minimum Viable Product.
• Assist with research related to customer-based analytical practice and develop communications for management and strategies for building institutional knowledge. Work with product and enterprise teams via both Agile And Waterfall Methodologies.
• Foster a `data-driven culture based on pragmatism and strategic decision through rigorous factual analysis at all levels. Should be willing to engage outside the immediate team to expand data science footprint throughout the organization.
KNOWLEDGE AND SKILLS:
• Must have a strong background in one or more of the following: Mathematical, Statistics, Probability, Deep Learning, Machine Learning, Natural Language Processing, Computer Vision, Recommendation Systems, Pattern Recognition, Large Scale Data Mining or Artificial Intelligence.
• Demonstrates a strong capacity for learning and assimilating new techniques, tools and methods.
• Comfortable with preparing or collaborating on presentation decks as well as delivering final stakeholder presentations as primary or supporting presenter.
• Has knowledge and can quickly ramp-up and leverage cloud-based cognitive service APIs such as Microsoft Azure, AWS and/or GCP.
• Is passionate about your work, but willing to support several projects at one time and can accept reprioritization as necessary.
• Comfortable delivering within an agile program.
•Masters degree required in Statistics, Operations Research, Mathematics, Economics, Computer Science, Engineering, Physics, Chemical Engineering or field of comparable foundations in mathematical and statistical analysis through the use of models, algorithms or programmed solutions.
• 5+ years experience with any of the following programming languages: R, Python, Java, C++, C#, Scala, SAS, MATLAB or similar scripting languages. Similar experience and proficiency with SQL are required.
• 5+ years experience across a breadth of data science, AI and machine learning disciplines including, but not limited to: forecasting, natural language processing (topic modeling, semantic search, text classification), deep learning and GPU-based algorithms (CNN, LSTM), computer vision
• 5+ years designing experiments and researching novel solutions beyond established literature or documentation
• Experience with data mining processes (SEMMA, CRISP-DM), data preparation, consolidation, imputation, transformation, interaction, variable reduction, modeling, maintenance, and post-mortem analysis.
• Experience with statistical methods such t-test of means, Tukey-HSD tests of means on groups, ANOVA, Proportion tests, data normalization and scaling, univariate and multivariate outlier detection.
• Experience with modeling techniques such as linear models, decision trees, neural networks, k-nearest-neighbor, support vector machines, cluster analyses, and ensembling methods.
• Strong Oral and Written skills.
• Experience with Agile Software Development.
• Experience in a large corporation or consulting firm with focus in marketing strategies, modeling, CRM and management sciences/statistics highly desired.
• Experience with Deep Learning tools and packages such as TensorFlow and Pytorch.
• Experience with frameworks and languages designed for big-data analysis, including Hadoop, Spark, Hive, and Pig.
• Experience with cloud computing frameworks or APIs such as Microsoft Azure, Amazon Web Services and Google Cloud Platform.
• Experience with developing, releasing, and maintaining machine-learning projects across the entire-ML lifecycle through sandboxed environments, logging, unit-testing, and including other software engineering best-practices.
• Must articles have published in a peer-reviewed scientific or academic journal, or filed patents in the fields of data science, artificial intelligence or machine learning.
•Ph.D. degree in a quantitative field preferred, but will also consider candidates that are currently enrolled in a Ph.D. program.