JOB SUMMARY The Data Scientist will be a key contributor in designing and implementing advanced pricing models that drive revenue performance, customer value, and competitive positioning. 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 - Git Hub, 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.