Senior Machine Learning Scientist

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.

Senior Machine Learning Engineer:

Expedia Technology teams partner with our Product teams to create innovative products, services, and tools to deliver high-quality experiences for travelers, partners, and our employees. A singular technology platform powered by data and machine learning provides secure, differentiated, and personalized experiences that drive loyalty and traveler satisfaction.

The Fraud & Risk team plays a pivotal role in safeguarding the company’s finances, thwarting billions of dollars in fraudulent attacks annually. Our efforts extend beyond financial security—we effectively combat various threats, including phishing attacks, counterfeit vacation rental schemes, improper payment diversions, and unauthorized access to personal and payment card information. By ensuring a secure environment, the team fosters trust among travelers and providers, making Expedia’s sustained revenue growth possible.

In this role, you will:

• Lead ML Solution Development:
Design, develop, and deploy machine learning models to solve complex business problems, ensuring alignment with strategic goals.

• Drive End-to-End ML Projects:
Manage the full ML lifecycle—from data exploration and feature engineering to model evaluation, productionization, and monitoring.

• Collaborate Across Functions:
Partner with engineering, product, and business teams to ensure ML solutions are technically sound and business-relevant.

• Mentor and Grow Talent:
Guide junior scientists and engineers, fostering a culture of innovation, learning, and technical excellence.

• Advance ML Capabilities:
Evaluate and implement cutting-edge algorithms and tools to enhance model performance, scalability, and robustness.

• Build and Maintain

Infrastructure: Configure and optimize storage, compute, and pipeline environments (cloud, on-prem, clusters) to support scalable ML workflows.

• Develop Internal Platforms and Best Practices:
Contribute to the evolution of internal ML platforms, reusable components, and standardized methodologies.

• Communicate with Impact:
Present findings and recommendations through clear, data-driven storytelling and compelling visualizations tailored to diverse audiences.

• Manage Stakeholders and Projects:
Lead cross-functional initiatives, manage expectations, and ensure timely delivery of high-impact solutions.

• Solve Strategically and Creatively:
Frame business problems as data science tasks, prioritize high-leverage work, and persist through technical and organizational challenges.

Experience and Qualifications

• Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field; or equivalent related professional experience

• 5+ years of experience building and deploying machine learning models in production environments.

• Experience applying sequential models (e.g., RNNs, Transformers) and/or Graph Neural Networks (GNNs) to solve real-world problems, with an understanding of their trade-offs and deployment considerations.

• ML Programming Expertise:
Proficient in at least one major ML language (e.g., Python, Scala) with familiarity across others.

• Software Engineering

Skills:

Applies design principles, data structures, and patterns to write clean, modular, and pipeline-ready code.

• Machine Learning Knowledge:
Deep understanding of supervised and unsupervised learning; working…