Data Scientist

Data Scientist III, Experimentation & Statistics

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

Why Join Us? 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.

About the Role

Are you passionate about experimental design, causal inference, and bridging the gap between statistical theory and real-world impact? We are looking for a Data Scientist specialising in experimentation science to lead the design and validation of our product experimentation methodologies.

This is not a generic data scientist or analyst role. We are seeking someone who:

• Has led the design, validation, and scaling of statistical methodologies for controlled experiments (not just “run A/B tests”)
• Excels at building or adapting frameworks for hypothesis testing, simulation, and error control in noisy, messy, production environments
• Can clearly explain experiment design, trade-offs, and findings to technical and non-technical stakeholders alike
• Brings scientific rigour and pragmatic creativity to complex, ambiguous challenges

What you will do

• Own the experimental methodology: Design, implement, and validate statistical frameworks for A/B and controlled experiments, including novel approaches for challenging business problems and partial compliance data
• Develop and evaluate simulation frameworks: Use Monte Carlo, bootstrapping, or other simulation methods to estimate power, sensitivity, and error rates of experimentation workflows before they go live
• Translate results for action: Communicate assumptions, trade-offs, and experiment outcomes (including limitations and risks) to engineers, product leaders, and business partners—regardless of statistical background
• Advance the discipline: Drive integration of new methods—from Bayesian inference and sequential testing to causal modelling—into our real-world experimentation platform
• Foster data-driven culture: Coach partners on statistical best practices, experimental design, and quality control

Minimum Qualifications

• Bachelor’s degree or higher in Statistics, Mathematics, Biostatistics, or a highly quantitative field—strong foundation in statistical theory is essential
• Demonstrated, hands-on experience designing (not just running) A/B or controlled experiments in production or business environments
• Direct experience with statistical methodologies, such as hypothesis testing, confidence intervals, error rate control, and handling biases/confounders
• Proficiency in at least one language for statistical analysis (Python, R, or PySpark), with the ability to develop and implement simulation or analysis frameworks
• Track record of clear, effective explanation of statistical concepts and results to non-technical and technical partners alike
• Proven ability to own projects end-to-end and influence product or business decisions

Preferred Qualifications

• Advanced degree (PhD or MSc) in Statistics or related field with an applied experimentation component
• Experience building or evolving experimentation tools/platforms (not just using off-the-shelf products)
• Experience with causal inference, Bayesian methods, or sequential/online testing in industry settings
• Publications, open-source contributions, or professional presentations on experimentation/statistics topics
• Prior experience in travel, marketplace, or e-commerce experimentation environments

What makes you successful in this role?

You thrive when you:

• Build new experiments from scratch and justify your methodological choices (rather than re-running legacy designs)
• Prioritise explanation and impact over jargon and buzzwords
• Translate statistical nuance for audiences at all levels, keeping communication as rigorous as your analysis
• Love collaborating in interdisciplinary teams and value transparency and scientific integrity.