Machine Learning Scientist III

Expedia Expedia · Hospitality · Seattle, WA +1

Expedia Group is seeking a Machine Learning Scientist III to design and implement ML solutions for customer experience in the online travel domain. The role involves taking ownership of the end-to-end ML lifecycle, from ideation and research to deployment and monitoring, focusing on complex post-booking recommendations, customer service, and trip management use cases. The scientist will tackle inference problems and multi-objective optimization, building core algorithms to anticipate traveler needs, personalize dynamic add-ons, and improve service experiences. Collaboration with product managers, engineers, and business stakeholders is key, as is a strong foundation in applied ML, experimental design, and causal inference.

What you'd actually do

  1. Design & Implement ML Solutions: Take ownership of the end-to-end ML lifecycle for your projects, from ideation and research to deployment and monitoring.
  2. Test, Learn, and Iterate: Design and analyze tests to validate your models and quantify their business impact and design future iterations.
  3. Collaborate and Communicate: Partner closely with product managers, engineers, and business stakeholders to understand requirements, define problems, and communicate your findings and results effectively.

Skills

Required

  • Python
  • SQL
  • supervised/unsupervised learning
  • deep learning
  • statistical modeling
  • experimental design (A/B testing)
  • causal inference
  • problem formulation
  • data sources
  • algorithm selection
  • evaluation strategy
  • production deployment
  • monitoring
  • software engineering best practices
  • clean, modular, maintainable code

Nice to have

  • reinforcement learning
  • GenAI/LLM technologies
  • translating research and academic papers into improved model designs and techniques
  • customer service domain knowledge
  • recommendation systems domain knowledge
  • operational applications of ML domain knowledge
  • e-commerce domain knowledge

What the JD emphasized

  • end-to-end ML solutions
  • machine learning software development lifecycle

Other signals

  • customer experience
  • recommendations
  • customer service
  • trip management
  • prediction and optimization
  • personalization
  • upsells