Lead Data Scientist (resiliency Engineering)

Expedia Expedia · Hospitality · Seattle, WA

Lead Data Scientist focused on Resiliency Engineering, applying ML and experimentation to improve system reliability and performance. The role involves end-to-end model development, productionization, and monitoring within high-availability environments, with a focus on integrating and operating AI/ML-enabled solutions to enhance platform and service health. The position emphasizes influencing technical direction through data-driven decisions and elevating standards for scientific rigor and observability.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • statistical modeling
  • machine learning
  • experimentation
  • data exploration
  • feature engineering
  • model selection
  • evaluation
  • productionization
  • monitoring
  • data modeling
  • API-aware solution integration
  • system design considerations
  • software engineering practices
  • modern programming
  • analytics tooling
  • data tooling
  • partnering with engineering and technical stakeholders
  • operationalize resilient solutions

Nice to have

  • advanced degree in data science, machine learning, statistics, computer science, or a related field
  • leading data science initiatives at scale in platform, infrastructure, or resiliency-focused environments
  • influencing architecture and technical direction
  • operational excellence
  • model observability
  • lifecycle management
  • experimentation quality
  • continuous improvement of production ML systems
  • using large-scale data and telemetry
  • inform strategic decisions
  • prioritize investments
  • improve system behavior, reliability, or customer-impacting outcomes
  • AI/ML-enabled solutions beyond core modeling
  • applying AI-driven tools, workflows, or techniques to accelerate insight generation
  • improve engineering effectiveness
  • enhance resilience-focused products and platforms

What the JD emphasized

  • production-grade solutions
  • operationalization
  • AI/ML-enabled solutions
  • AI-driven systems, tools, or workflows
  • applying AI/ML concepts to real world products
  • observability
  • model observability
  • lifecycle management
  • continuous improvement of production ML systems

Other signals

  • improving system reliability
  • improving system performance
  • operational outcomes
  • scalable data science approaches
  • end-to-end model and solution development
  • productionization
  • ongoing monitoring
  • resilient, high-availability environments
  • operationalization of analytical products
  • AI/ML-enabled solutions
  • AI-driven systems, tools, or workflows
  • applying AI/ML concepts to real world products
  • elevating standards for scientific rigor
  • observability
  • reusable approaches