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Machine Learning Scientist II - Search
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United Kingdom - London
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Technology
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Full-Time Regular
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11/12/2025
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ID # R-99261
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.
Machine Learning Scientist II
Our Expedia Product & Technology division builds innovative products, services, and tools to deliver high-quality experiences for travelers, partners, and our employees. A unified, singular technology platform powered by data and machine learning provides secure, differentiated, and personalized experiences for the traveler and our partners that drive loyalty and customer satisfaction.
The Machine Learning Scientist II role sits on the Lodging Search Ranking AI team in the Expedia Product & Technology division of Expedia Group. At the heart of the 3-side marketplace (the traveler, the property owners and the platform), this team develops and optimizes ranking models with state-of-the-art machine learning / genAI techniques to power lodging search and personalized lodging ranking/recommendations for the multiple brands and lines of business in our portfolio.
In this role, your expertise and passion for innovation, developing cutting-edge technology and implementing industry-leading solutions, will improve the experience of millions of travelers and travel partners each year. This is an applied research role: your models will be deployed to our production systems, and your results will be measured objectively via A/B testing, directly impacting our business results. We collaborate closely with the analytics, product, and engineering teams.
What you’ll do:
Work with product management to understand business problems, identify challenges and machine learning opportunities, and scope solutions
Conduct exploratory data analyses, formulate machine learning problems, and build effective models
Partner with data and software engineering teams to deliver your solutions into production
Document the technical details of your work
Present your ideas and results to product management, stakeholders, and leadership teams in a clear and effective manner
Collaborate and brainstorm with other team members and across the company
Minimum Qualifications:
Master's degree or Ph.D. in Computer Science, Statistics, Math, Engineering, or a related technical field; or equivalent related professional experience.
Solid understanding of modern machine learning techniques, statistics, and hypothesis testing
Hands-on experience with data wrangling, feature engineering, model building, and visualization.
Expertise with Python, related machine learning tools (TensorFlow or PyTorch), and SQL-like query languages for data extraction, transformation, and loading.
Good programming practices, with the ability to write readable, fast code.
Passion for solving interesting and impactful real-world problems using principled techniques and best practices.
Intellectual curiosity and a desire to learn new techniques and technologies.
Preferred Qualifications:
Experience in recommender systems.
Experience working with large data sets in a distributed computing environment such as Spark.
Accommodation requests
If you need assistance with any part of the application or recruiting process due to a disability, or other physical or mental health conditions, please reach out to our Recruiting Accommodations Team through the Accommodation Request.
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