Miami, United States
11 months ago
The Senior Data Scientist, Revenue Management is responsible for the development for all decision support tools/models used by revenue management team and for executing them efficiently. In addition Manager is responsible for optimizing and enhancing the suite of revenue management tools used to maximize $4 billion in ticket revenue. This employee will also perform regular statistical analysis and present results to senior leadership in a business format to assist in decision making and analysis.
Essential Duties and Responsibilities:
- Develop and maintain demand forecasting model (time series, regression, segmentation models etc.)
- Implement non-linear regression models to develop price elasticity models
- Knowledge of linear and integer optimizations, and able to integrate demand forecasting and price elasticity to develop revenue optimization models
- Create and leverage algorithmic modeling for International ticket revenue optimization while working with the Revenue Product team to understand and quantify effects of pricing/policy changes.
- Design and develop models and algorithms that drive performance and provide insights, from prototyping to production deployment, across key areas of interest (e.g., APD optimization, Load Factor optimization, Cabin mix, channel mix)
- 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.
- Owns and manages of the various components of Data Science Life cycle: Data Wrangling, Feature Engineering, Data Visualization (discovery), Model Generation, Implementation and Maintenance.
- Manage the relationship with other analytical teams in the company and IT to design, and implement analytical projects
- Identify business needs, translate business needs into technical requirements, and implement analytical models to meet project deadlines.
- Continuously seek out opportunities to further develop our analytical, engineering, statistical, etc. toolkit & team
- Master’s degree in Operations Research, Industrial Engineering, Statistics or Management Science is preferred.
- Bachelor’s degree from four-year College or university required (in Operations Research, Industrial Engineering, Statistics, Management Science or related field).
- Post-grad experience as a Data Scientist, preferably 3- 5 years of experience, including experience designing statistical models.
- Equivalent combination of education and experience will be considered.
- Experience managing a team preferred.
- Strong leadership skills.
- Expertise in building and applying statistical/mathematical methods, machine learning/predictive modelling with real-world use cases and experience with tools such as R, SAS, SPSS.
- Proficient with SQL and knowledge of BI related principles such as ETL, data modeling, & data warehousing.
- Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner to both technical and non-technical audiences with a desire to work in a collaborative, intellectually curious environment with the ability to interact across various teams.
- Ability to manage multiple projects simultaneously with deadlines and manage changing priorities with minimal supervision and intervention.