Lead Data Scientist (Resiliency Engineering)

Expedia•Seattle, WA

19h•$116,500 - $186,500•Hybrid

About The Position

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. 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.

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.

Nice To Haves

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.

Responsibilities

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.