Machine Learning Scientist III - Pricing

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.

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 know that when one of us wins, we all win.

We provide a full benefits package, including exciting travel perks, generous time-off, parental leave, a flexible work model (with some pretty cool offices), and career development resources, all to fuel our employees' passion for travel and ensure a rewarding career journey. We’re building a more open world. Join us.

As part of the Machine Learning Science organization at Expedia Group, you’ll help build a scalable, intelligent pricing system that directly shapes how we balance volume and profitability across multiple lines of business. The decisions this system drives influence billions of dollars in revenue, and the work is visible at the highest levels of the company.

As a Machine Learning Scientist III, you’ll help define this system. You’ll tackle end-to-end pricing problems alongside a sharp team of ML scientists, data scientists, engineers, product managers, and operations analysts. You will contribute to an area of active scientific research: causal inference, mixed integer programming, and ML-based demand estimation.

• Design, build, and improve ML models and systems that power Expedia Group products, with a focus on measurable business and customer impact
• Perform end-to-end model development, including problem formulation, data exploration, feature engineering, training, evaluation, and deployment in production environments
• Develop robust data pipelines, data transformations, and data quality checks to ensure high-quality input signals for ML models and experimentation
• Partner with product, engineering, and analytics teams to translate business problems into ML solutions, define success metrics, and run experiments to validate impact
• 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
• Contribute to system design (including model-serving APIs and supporting data models), documentation, and best practices that enable reuse and scalability across multiple product domains

• Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related technical field, or equivalent practical experience
• Professional experience as a Machine Learning Scientist or in a similar applied ML role, working on end-to-end model development and deployment in real‑world products
• Proficiency in at least one programming language commonly used for ML (such as Python) and experience working with data processing frameworks, model training libraries, and model evaluation techniques
• Experience owning ML components or services in production, including monitoring model performance, maintaining data pipelines, and collaborating with engineering teams on APIs and data models

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

The total cash range for this position in Seattle is $ to $. Employees in this role have the potential to increase their pay up to $, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the…