Lead Data Scientist (Resiliency Engineering)

Requirements

• Degree in a quantitative or technical field (Bachelor's, Master's, or PhD) in data science, machine learning, statistics, computer science, or a related discipline.
• Demonstrated ownership of complex data science solutions across a domain or organization-level scope, with experience solving ambiguous problems and driving measurable business or platform outcomes.
• Strong technical foundation in machine learning, statistical analysis, experimentation, data modeling, and software engineering practices for production-grade solutions, including system design considerations and integration with service-based environments.
• Experience building and deploying scalable data products or models using modern programming, analytics, and data tooling, with the ability to operate effectively across multiple technical domains.
• Experience partnering with engineering and technical stakeholders to operationalize resilient solutions, monitor performance, and improve reliability through data-informed insights and model-driven recommendations.
• Advanced degree in data science, machine learning, statistics, computer science, or a related field.
• Experience leading data science initiatives at scale in platform, infrastructure, or resiliency-focused environments, including influencing architecture and technical direction within a domain.
• Demonstrated strength in operational excellence, including model observability, lifecycle management, experimentation quality, and continuous improvement of production ML systems.
• Experience using large-scale data and telemetry to inform strategic decisions, prioritize investments, and improve system behavior, reliability, or customer-impacting outcomes.
• Experience with AI/ML-enabled solutions beyond core modeling, including applying AI-driven tools, workflows, or techniques to accelerate insight generation, improve engineering effectiveness, or enhance resilience-focused products and platforms.
• The total cash range for this position in Seattle is $116,500.00 to $163,000.00. Employees in this role have the potential to increase their pay up to $186,500.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.
• Starting pay for this role will vary based on multiple factors, including location, available budget, and an individual's knowledge, skills, and experience. Pay ranges may be modified in the future.

Benefits

Additional Information

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. In this role, you will: Lead the design, development, and deployment of data science solutions for Resiliency Engineering, applying statistical modeling, machine learning, and experimentation to improve system reliability, performance, and operational outcomes. Translate ambiguous technical and business problems into scalable data science approaches, partnering across engineering and domain teams to define solution strategy, success metrics, and measurable impact. Drive end-to-end model and solution development, including data exploration, feature engineering, model selection, evaluation, productionization, and ongoing monitoring within resilient, high-availability environments. Apply strong technical depth across multiple domains, including data modeling, API-aware solution integration, system design considerations, and operationalization of analytical products that support platform and service health. Safely integrate and operate AI/ML-enabled solutions that improve outcomes, including familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products. Influence technical direction through data-driven decision making, elevating standards for scientific rigor, observability, and reusable approaches that can be applied across services, domains, and organizational needs.

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