The EG Advertising Machine Learning team sits at the heart of Expedia Group’s global advertising marketplace—a fast-growing, high-impact business at the intersection of travel, technology, and commerce. The team develops the predictive models that power our platform, including click-through rate and booking propensity models, as well as deeper insights into advertiser bidding behaviour and broader marketplace dynamics. It’s a unique environment where machine learning directly shapes real-time auctions, personalised experiences, and large-scale optimisation across a complex two-sided ecosystem. 

As a Machine Learning Scientist III, you’ll play a key role within this team, applying machine learning to connect millions of travellers with thousands of partners. One of the most exciting aspects of the role is the end-to-end visibility across both auction and personalisation systems, allowing you to drive meaningful, system-wide impact rather than focusing on a single component. Working closely with product and engineering partners, you’ll combine deep data exploration with strong business context to design and deliver scalable, production-ready solutions that directly influence user experience and marketplace performance. 

 
In this role, you will: 

•  Design, develop, and apply machine learning, statistical, and optimization models to solve business problems in EG Advertising, including predictive modelling for ad auction systems (e.g., click-through rate, booking propensity, and advertiser bidding dynamics), improving both partner and traveller outcomes. 

• Translate ambiguous business questions into measurable scientific problems, defining success metrics and delivering data-driven recommendations grounded in experimentation, deep data exploration, and marketplace understanding. 

• Partner closely with engineers, product managers, analysts, and business stakeholders to productionize models, influence roadmap decisions, and drive adoption of ML-powered solutions across both partner-facing and traveller-facing systems. 

• Build and evaluate scalable approaches across multiple technical domains, including feature engineering, model development, experimentation design (A/B testing), data modelling, and integration into production services within large-scale distributed systems. 

• Safely integrate and operate AI/ML-enabled solutions that improve outcomes, including applying modern ML techniques and selective use of GenAI/agentic AI approaches to enhance modelling, experimentation, and system capabilities. 

• Contribute strong technical judgment through documentation, code reviews, model monitoring, and operational best practices that support reliable, scalable, and reusable scientific solutions across the advertising marketplace.  

Minimum Qualifications: 

•  Advanced degree (MS or PhD) in Machine Learning, Computer Science, Statistics, Applied Mathematics, Economics, or a related field. 

• 5+ years of relevant industry experience applying machine learning, statistical modelling, experimentation, or optimisation techniques in production environments. 

• Demonstrated ownership of end-to-end machine learning solutions at the service, multi-service, or domain level, with accountability for model quality, business impact, and operational reliability. 

• Strong technical foundation in machine learning methods, experimental design (A/B testing), data analysis, and programming for production-quality solutions working with large-scale datasets. 

• Proficiency in Python and its ML/data ecosystem (e.g., PySpark, pandas, TensorFlow, PyTorch or similar) and strong SQL skills. 

• Experience partnering cross-functionally to frame problems, communicate trade-offs, and deliver solutions from ideation through production. 

• Strong first-principles problem-solving skills and ability to clearly communicate complex technical concepts. 

• Demonstrated strength in architecture and design of ML systems, including data modelling, experimentation frameworks, and production integration patterns.  

Preferred Qualifications: 

• Experience building and scaling machine learning systems for advertising, ranking, recommendation, auction optimisation, pricing, or forecasting. 

• Experience working in or a strong understanding of two-sided marketplaces, online advertising, or e-commerce ecosystems. 

• Experience using data and model insights to influence product strategy and business decisions. 

• Hands-on exposure to GenAI, LLMs, or AI-driven tooling that supports faster experimentation, improved modelling, or better product outcomes.