Data Scientist III - Experimentation Science; Statistical Modelling

Expedia Group brands power global travel for everyone, everywhere. We design cutting‑edge tech to make travel smoother and more memorable, and we create ground‑breaking solutions for our partners. Our diverse, vibrant, and welcoming community is essential in driving our success.

To shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and know that when one of us wins, we all win.

We provide a full benefits package, including exciting travel perks, generous time‑off, parental leave, a flexible work model (with some pretty cool offices), and career development resources, all to fuel our employees’ passion for travel and ensure a rewarding career journey. We’re building a more open world. Join us.

As a Data Scientist III on our Experimentation Science team, you will play a critical role in shaping the statistical methodologies that underpin Expedia Group’s experimentation platform. Your work will ensure that every feature change on our website—from button colors to booking flows is rigorously tested to maximize business impact. This role blends research and practical application, requiring you to design, evaluate, and improve hypothesis testing frameworks and experimentation techniques.

You will collaborate closely with platform engineers to implement scalable solutions, provide expert guidance to stakeholders, and drive innovation through cutting‑edge statistical research. This position offers a unique opportunity to apply deep statistical expertise in a dynamic environment where your contributions directly influence product decisions and customer experiences worldwide.

• Lead the development and validation of advanced statistical methodologies to support Expedia Group’s experimentation platform, ensuring robust and reliable A/B testing across the website’s features and user experiences.
• Collaborate closely with the experimentation platform team to design, implement, and scale new testing frameworks and tools that enhance the platform’s capabilities and reporting accuracy.
• Provide expert guidance and training to cross‑functional teams on experimental design, hypothesis testing, and statistical best practices to optimize test duration, sample size, and overall experiment quality.
• Conduct original research to explore and develop innovative approaches for experimentation challenges, including designing proof‑of‑concept studies and integrating new methodologies into production workflows.
• Analyze complex experimental data, build simulation frameworks to quantify error rates, and communicate findings clearly to both technical and non‑technical stakeholders to inform business decisions.

• Deep expertise in statistical methodologies, particularly hypothesis testing, design of experiments, and interpretation of p‑values and confidence intervals, with the ability to apply these concepts rigorously in an experimentation context.
• Strong analytical mindset with the capability to structure complex business problems and translate them into statistically sound experimental designs and solutions.
• Proficiency in programming with experience in Python, PySpark, R, or similar languages, sufficient to implement and test statistical models and simulations, with an emphasis on correctness over advanced coding skills.
• Demonstrated ability to conduct independent research, quickly learn new statistical techniques, and develop proof‑of‑concept models that can be scaled and integrated into production experimentation platforms.
• Excellent communication skills to clearly explain complex statistical concepts and experimental results to both technical and non‑technical stakeholders, ensuring alignment and understanding across teams.

• A bachelor’s degree or higher in Mathematics, Statistics, or a closely related quantitative field, providing a strong foundation in statistical theory and experimental design.
• Proven expertise in hypothesis testing, design of…