Lead Statistician, TSS in Miramar, FL at Royal Caribbean Cruises Ltd.

Position Summary:

Spearheads the analysis and examination of the business to define needs and/or opportunities; leading the gathering, and analysis of data and formulating a recommended course of action. The primary area of focus is leveraging existing Data Science initiatives or deploying new models or AI technologies. This role is responsible for seeing projects through from start to finish while leading a team of analysts and managing resources across the technical divide. Has responsibility for all areas within the global contact centers to define metrics that will assist the business with goals as set forth by organization leaders in a global context. Understands, analyzes and documents data to deliver informative, well-organized presentations to senior and executive management regarding processed forms i.e. guest satisfaction, enrollments, guest demographics, employee satisfaction, etc. Leads the development of functional and system specifications.

Essential Duties & Responsibilities:

• Develop and maintain demand forecasting model (time series, regression, segmentation models etc.) 
• Use non-linear regression models to develop price elasticity models
• Knowledge of linear and integer optimizations
• Knowledge of Machine Learning, Deep Learning, Neural Networks
• Knowledge of Voice Analytics and Text Mining
• Manage the relationship with other analytical teams in the company and IT to design, and implement analytical projects
• Project management experience to identify business needs, translate business needs into technical requirements, and implement analytical models to meet project deadlines. 
• Perform algorithmic modeling for Royal Caribbean contact center workforce management and work closely with the OCU and finance teams.
• Integrate with external data sources to discover interesting trends.
• Design rich data visualizations to communicate complex ideas to internal and external teams.
• Design and manage data QA and validation using automation and best practices.
• Collaborate directly with teams/individuals across the company to facilitate the design, research, development, and delivery of data statistics, models and client deliverables.
• Take ownership of the various components of Data Science Life cycle: Data Wrangling, Feature Engineering, Data Visualization (discovery), Model Generation, Implementation and Maintenance.
• Continuously get better - seek out opportunities to further develop our analytical, engineering, statistical, etc. toolkit
• Respond to ad-hoc data and analysis needs while supporting various projects. Conduct analyses of business data, evaluate trends and pattern changes, and learn to disperse results in a format appropriate for the audience.