Location
London, City of

Hours
Full Time

Salary
Competitive, commensurate with experience

About the Role
At Expedia Group, we help travelers explore the world, one journey at a time. As a global travel company powered by passionate people, trusted partnerships, and leading technology, we connect travelers, partners, and advertisers through our consumer brands, B2B network, and travel advertising business. Here, you'll do meaningful work that helps millions of people discover, book, and experience travel with more ease, confidence, and joy. Our five Behaviors - Traveler First, Think Big, Operate with Excellence, Ownership Mindset, and Succeed Together - foster a supportive environment where people can grow their careers and have the flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere.

Our Expedia Product & Technology division builds innovative products, services, and tools to deliver high-quality experiences for travelers, partners, and employees. A unified technology platform powered by data and machine learning provides secure, differentiated, and personalized experiences that drive loyalty and customer satisfaction.

The Machine Learning Scientist II role is part of the Lodging Search Ranking AI team within the Expedia Product & Technology division. This team develops and optimizes ranking models using state-of-the-art machine learning and generative AI techniques to power lodging search and personalized lodging ranking and recommendations across multiple brands and lines of business.

In this applied scientist role, your models will be deployed to production systems and measured objectively via A/B testing, directly impacting business results. You will collaborate closely with analytics, product, and engineering teams to deliver impactful solutions.

Key responsibilities include:
- Develop, implement, and optimize machine learning models powering data-driven features and products, from problem framing through production deployment and iteration.
- Design and evaluate experiments, offline evaluations, and A/B tests using statistical rigor to measure model impact and drive decisions.
- Collaborate with engineers, product managers, and analysts to translate ambiguous business problems into well-scoped ML solutions, including data requirements, modeling approach, and success metrics.
- Apply strong data modeling, feature engineering, and model selection skills across multiple domains, ensuring models are robust, explainable, and performant at scale.
- Safely integrate and operate AI/ML-enabled solutions, including monitoring model performance, detecting degradation, and driving continuous improvements in production.
- Contribute reusable methodologies and best practices for AI-driven systems and workflows that can be leveraged across teams and problem spaces.

Experience
- Minimum 2+ years of relevant professional experience in machine learning or applied science.
- Proven ability to own ML solutions for a well-defined service or product area, including data exploration, model development, offline and online evaluation, and partnering with engineering for integration.
- Proficiency in at least one major programming language used for ML (such as Python) and common ML/AI frameworks and tooling.
- Solid grounding in core ML concepts such as supervised and unsupervised learning, model generalization, overfitting, and evaluation metrics.
- Experience working with real-world, noisy datasets.

About you
- Passionate about innovation and developing cutting-edge technology.
- Strong collaborator who works effectively with cross-functional teams including analytics, product, and engineering.
- Skilled in designing robust experiments and data-driven decision making.
- Familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real-world products.
- Demonstrated ability to deliver impactful ML solutions that improve user experience and business outcomes.

Qualifications
- Bachelor’s degree in Computer Science or a related technical field, or equivalent professional experience.
- Preferred: Advanced degree (Master’s or PhD) in a quantitative field focused on machine learning, statistics, or AI.
- Preferred: Experience designing and operating ML systems at scale, including feature pipelines, model training workflows, and online inference.
- Preferred: Experience with recommendation systems, ranking, search, personalization, forecasting, or optimization models.
- Demonstrated track record of leading the end-to-end lifecycle of ML solutions from ideation through experimentation, launch, and ongoing optimization.