Data Scientist

• The role involves full lifecycle model development, including data exploration, feature engineering, model building, simulation, and integration with pricing systems.
• DUTIES & RESPONSIBILITIES Develop and deploy machine learning and statistical models to support pricing decisions, demand forecasting, and revenue optimization.
• Conduct data exploration and wrangle large, complex datasets related to bookings, customer behavior, ship inventory, pricing, promotions, and competitive dynamics.
• Collaborate with revenue management, analytics, and finance teams to define modeling requirements, test model scenarios, and ensure solutions align with business goals.
• Apply pricing science and economics concepts to improve segmentation, elasticity estimation, and inventory control decisions.
• Assist in building simulation tools and pricing engines that operationalize models in real-time or batch environments.
• Collaborate with data engineers to define data requirements and ensure model-ready pipelines.
• Document methodologies, assumptions, and model limitations clearly for technical and business audiences.
• Guide data developers to ensure data is accurate and consumable for data science projects Provide executive-level summaries, visualizations, and recommendations for the senior leadership team.
• Stay current on data science best practices and tools.
• Perform other job-related duties as assigned.
• QUALIFICATIONS DEGREE TYPE: Bachelor''s Degree FIELD(S) OF STUDY: Data Science, Statistics, Economics, Operations Research, Computer Science, Applied Mathematics, or related field.
• EXPERIENCE Minimum 2 years of experience in a data science or quantitative role, ideally with exposure to pricing, forecasting, or optimization.
• COMPETENCIES & SKILLS Experience building models using Python, R, or similar programming languages.
• Hands-on experience with SQL and large-scale data manipulation.
• Familiarity with optimization libraries, simulation frameworks, or revenue management systems is a plus.
• Familiarity with MLOps practices, including model monitoring, deployment pipelines, and reproducibility.
• Strong foundation in predictive modeling, time series, and price elasticity estimation.
• Working knowledge of pricing concepts such as demand curves, overbooking, dynamic pricing, and segmentation.
• Experience with version control - GitHub, cloud environments - AWS, and productionizing models is a plus.
• Ability to communicate clearly with both technical and non-technical stakeholders.
• A self-starter with strong problem-solving skills and a team-first mindset.

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