Machine Learning Scientist III - Package Pricing

Expedia Expedia · Hospitality · Seattle, WA +1

Expedia Group is seeking a Machine Learning Scientist III to design, build, and improve ML models and systems for their package pricing system. This role involves end-to-end model development, including data pipelines, feature engineering, training, evaluation, and deployment in production. The scientist will partner with cross-functional teams to translate business problems into ML solutions and contribute to system design for scalability. Experience with pricing, revenue optimization, causal inference, or optimization techniques is preferred.

What you'd actually do

  1. Design, build, and improve ML models and systems that power Expedia Group products, with a focus on measurable business and customer impact
  2. Perform end-to-end model development, including problem formulation, data exploration, feature engineering, training, evaluation, and deployment in production environments
  3. Develop robust data pipelines, data transformations, and data quality checks to ensure high-quality input signals for ML models and experimentation
  4. Partner with product, engineering, and analytics teams to translate business problems into ML solutions, define success metrics, and run experiments to validate impact
  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

  • Python
  • data processing frameworks
  • model training libraries
  • model evaluation techniques
  • owning ML components or services in production
  • monitoring model performance
  • maintaining data pipelines
  • collaborating with engineering teams on APIs and data models

Nice to have

  • Advanced degree (master’s or PhD) in a quantitative discipline
  • pricing
  • revenue optimization
  • marketplace dynamics
  • designing and analyzing experiments (e.g., A/B testing)
  • working with noisy or incomplete data
  • building ML models using user behavior, segmentation, or contextual signals
  • causal inference methods
  • optimization techniques (e.g., mixed-integer programming)

What the JD emphasized

  • end-to-end model development
  • deployment in production environments
  • ML models and systems that power Expedia Group products
  • pricing, revenue optimization, marketplace dynamics, or similar business problems

Other signals

  • end-to-end model development
  • deployment in production environments
  • ML models and systems that power Expedia Group products
  • pricing, revenue optimization, marketplace dynamics