Data Scientist III, Analytics (B2B Supply Optimisation)

At Expedia Group, we help travelers explore the world, one journey at a time. As a global travel company powered by passionate people, trusted partnerships, and leading technology, we connect travelers, partners, and advertisers through our consumer brands, B2B network, and travel advertising business.

Here, you'll do meaningful work that helps millions of people discover, book, and experience travel with more ease, confidence, and joy. Our five Behaviors—Traveler First, Think Big, Operate with Excellence, Ownership Mindset, and Succeed Together—help foster a supportive environment where people can grow their careers and have the flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere.

Expedia B2B

Expedia B2B is the B2B arm of Expedia Group. We bring Expedia Group's innovative technology and distribution solutions to partners across the world. These businesses include global financial institutions, corporate managed travel, offline travel agents, global travel suppliers (like major airlines) and many more.

Our Analytics Team

Our Analytics team is at the heart of supply optimisation strategy, turning complex data into meaningful decisions that improve outcomes for our global B2B partners. As a Data Scientist III, Analytics, you will operate largely independently, applying and enhancing analytical best practices to solve sophisticated business problems. You will manage analytical workstreams, mentor junior colleagues, and engage regularly with stakeholders up to VP level.

In this role, you will:

• Apply advanced statistical and machine learning techniques to supply optimisation challenges, delivering data-driven insights and recommendations that create measurable business impact.
• Extract, structure, and transform data from multiple sources independently to build datasets suited for modelling and in-depth analysis.
• Design and execute measurement frameworks—including A/B testing, causal impact analysis, and multivariate methods—selecting the appropriate technique based on the business question and clearly communicating trade-offs.
• Build, evaluate, and iterate on statistical models (e.g., regression, clustering, classification), correctly interpreting outputs and translating findings into actionable recommendations.
• Develop clear, audience-appropriate data visualisations and narratives that communicate insights to both technical and non-technical stakeholders.
• Lead small analytical workstreams end-to-end, partnering with stakeholders to refine requirements, agree on scope, and evolve the approach based on findings.
• Automate repeated measurement and reporting tasks and build scalable dashboards, enabling self-serve analytics for stakeholders across the business.
• Produce high-quality project artefacts—including technical documentation, presentations, and executive summaries—tailored to the appropriate forum and audience.
• Collaborate openly with analytics peers, domain experts, and business stakeholders to validate approaches, share knowledge, and socialise findings.
• Provide coaching and constructive feedback to junior team members on statistical techniques, visualisation best practices, and data quality standards.
• Champion reproducibility by writing shareable, well-documented code and contributing to shared repositories such as GitHub or Confluence.