Machine Learning Scientist III

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 when one of us wins, we all win.

Our Machine Learning and Data Science team is growing! We are looking to hire researchers and data scientists who want to break new ground tackling complex customer experience problems in the travel domain. Your focus will be on developing state‑of‑the‑art machine learning algorithms to power and enhance the customer experience across post‑booking recommendations, customer service, and trip management.

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

Experience & Education

• PhD or MS in a quantitative field (Computer Science, Economics, Statistics, Physics).
• 3+ years of hands‑on industry experience building and deploying ML models to solve real‑world problems.

Functional & Technical Skills

• Expertise in applied ML: Deep practical knowledge of machine learning theory, statistical modeling, experimental design (A/B testing), causal inference, and the end‑to‑end ML solution lifecycle.
• Technical Fluency: Strong programming skills in Python (pandas, scikit‑learn, py Spark) and SQL; writes clean, maintainable code and follows software engineering best practices.
• First‑Principles Problem Solver: Able to dissect ambiguous problems and communicate complex technical ideas clearly.

Highly Desired Experience

• Domain knowledge in customer service, recommendation systems, operational ML applications, and/or e‑commerce.
• Experience with reinforcement learning or other advanced ML techniques.
• Experience building and deploying models using GenAI/LLM technologies.
• Experience translating research and academic papers into improved model designs.

• Bachelor’s degree in Computer Science or a related technical field; or equivalent professional experience.
• 5+ years of relevant professional experience.
• Proven ability to design end‑to‑end ML solutions, including problem formulation, data preparation, feature engineering, evaluation strategy, and production deployment and monitoring.
• Strong programming skills in Python, pandas, scikit‑learn, PySpark, and SQL; follows software engineering best practices and contributes to shared codebases.
• Familiarity with AI‑driven systems, tools, or workflows and the machine-learning software development lifecycle from experimentation through operational monitoring.

• MS or PhD in a quantitative field (Computer Science, Economics, Statistics, Physics, or related discipline).
• 3+ years of hands‑on industry experience building, deploying, and iterating machine-learning models that solve real‑world problems in production environments.
• Proven ability to design end‑to‑end ML solutions, including problem formulation, data preparation, feature engineering, evaluation strategy, and production deployment and monitoring.
• Strong programming skills in Python and its data science ecosystem (pandas, scikit‑learn, PySpark) and proficiency in SQL; follows software engineering best practices and contributes to shared codebases.
• Familiarity with AI‑driven systems, tools, or workflows and applying AI/ML concepts to real-world products, including experience with the machine-learning software development lifecycle from experimentation through operational monitoring.

We provide a full benefits package, including travel perks, generous time‑off, parental leave, a flexible work model, and career development resources.

The total cash range for this position in Seattle is $ to $. Employees in this role have the potential to increase their pay up to $, based on performance. The…