Senior Data Scientist Ancillaries

Job description

**Position Description For Data Scientist, Ancillaries** Sabre Ancillaries Delivery members are part of a techno-functional team specialized in implementing and delivering Ancillary Optimization, Offer Optimization, and Dynamic Pricing solutions for airlines. The Data Scientist is recognized as a subject matter expert in data science, experimentation, model performance, and airline commercial business practices, helping customers adopt and optimize Ancillaries solutions. **Responsibilities** - Lead end-to-end data science projects, from problem definition to deployment and monitoring - Oversee the complete solution implementation lifecycle, including project kickoff, business process assessment, and transition to customer care. - Gain comprehensive knowledge of user interfaces to train analysts and support integration with airline business processes. - Understand dynamic pricing and offer optimization models, collaborating with operations research teams for improved delivery to airlines. - Design and deploy machine learning and optimization models to enhance performance and customer outcomes. - Collaborate across all stages of implementation, from discovery to validation and customer care. - Develop and refine predictive and statistical models for complex business challenges. - Convert business needs into analytical frameworks, KPIs, and measurable results. - Analyse large datasets and build reliable pipelines. - Work with engineering and product teams to integrate models into production systems. - Clearly communicate insights and recommendations to technical and non-technical audiences. - Guide junior data scientists and promote best practices and innovation. **Required Experience / Skills** - Experience in data science, machine learning, optimization, or analytics roles, preferably within airline retailing, ancillaries, pricing, or revenue management. - Good understanding of airline ancillary products, offer management, and commercial optimization concepts is preferred. - Experience building, evaluating, and operationalizing machine learning or optimization models using large and complex datasets. - Strong analytical and problem-solving skills with the ability to convert business questions into data-driven solutions. - Demonstrated ability to apply statistical, modelling, and analytical techniques to solve real-world travel or commercial business problems. - Experience with experimentation, A/B testing, feature engineering, model monitoring, and performance measurement. - Experience interpreting model outputs and translating findings into business recommendations for stakeholders. - Knowledge of machine learning, forecasting, optimization, or pricing models. - Good SQL skills and experience working with relational and non-relational databases. - Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative field desired. - Must be highly organized, able to manage multiple priorities, and comfortable working in a fast-paced environment. - Prior experience with airline forecasting, optimization, personalization, or retailing use cases is an advantage. - Proficient English written and verbal communication skills; ability to explain technical concepts to non-technical audiences. - Ability to identify issues, assess business impact, and determine when escalation is needed. - Willingness to travel as needed to support customer engagements and business priorities. **Preferred Technical Skills** - Microsoft tools: Excel, PowerPoint, Word, and data visualization tools for analysis and presentation. - Tools: Python, R, SQL Developer, Jupyter notebooks, and analytics or experimentation platforms. - Databases and platforms: Google BigQuery, MongoDB, and other cloud-based analytics environments. - Operating systems: UNIX, Linux, and Windows. - Experience with version control, scripting, and programming languages such as Python, Java, or C++ is a plus.