Data Scientist

Machine Learning Scientist III, PersonalisationExpedia 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.

Introduction to the teamThe Unified Personalisation Service team is part of Expedia Product & Technology. UPS is building Expedia Group's centralized, real-time personalisation engine across brands and channels, powering ranking, recommendations, retrieval, and other adaptive experiences that help travelers see more relevant, contextual, and useful experiences throughout their journey.

We are looking for a Machine Learning Scientist III to help build production ML systems for personalisation, with emphasis on deep learning, neural recommender systems, sequential and session-based modelling, embeddings, scalable experimentation, and reliable model deployment.

This is a hands-on applied science and engineering role for someone who can contribute across model development, experimentation, data pipelines, deployment, and production model quality.

In this role, you willDevelop, apply, and advance machine learning solutions for personalisation use cases, translating business and customer problems into scalable scientific approaches and production-ready models.

Design experiments, evaluate model performance, and use data-driven methods to improve relevance, ranking, recommendation, and overall customer experience across personalisation systems.

Partner across engineering, product, analytics, and science teams to define solution approaches, influence technical direction, and deliver ML capabilities that can operate across multiple products and domains.

Contribute technical depth in model development, feature design, data preparation, offline and online evaluation, and the operationalisation of machine learning solutions in production environments.

Apply strong technical judgment to system design, API design, data modelling, and low-level solution design that support robust, maintainable, and extensible ML-powered services.

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.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Machine Learning, Statistics, Mathematics, a related technical field, or equivalent professional experience.5+ years of relevant experience in machine learning, applied science, data science, or software development, including delivering production-grade ML solutions.
• Demonstrated ownership of machine learning solutions within a service, multi-service, or domain-level scope, with accountability for model quality, experimentation, and operational performance.
• Strong foundation in machine learning methods, statistical analysis, experimentation, feature engineering, and working with large-scale datasets in production environments.
• Proficiency in software engineering practices for scientific systems, including coding, low-level design, API design, data modelling, and collaboration with engineering teams to productionise solutions.

Preferred Qualifications

• Advanced degree in Machine Learning, Computer Science, Statistics, Mathematics, or a related technical field.
• Experience building and scaling personalization, recommendation, ranking, retrieval, or relevance models in large, complex consumer-facing environments.
• Experience with neural recommendation systems, sequential or session-based recommendation, transformer-based recommenders, semantic retrieval, or representation learning at scale.
• Experience with foundation models, LLMs, embedding models, semantic IDs, hybrid LLM-recommender systems, or retrieval-augmented personalisation workflows.
• Demonstrated ability to use data, metrics, and experimentation to guide prioritisation and decision-making while balancing scientific rigour, product impact, and platform scalability.
• Experience with production ML workflows such as model serving, experimentation frameworks, feature or data pipelines, monitoring, model lifecycle management, or MLOps.