Senior Machine Learning Scientist

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-help 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.

Senior Machine Learning Scientist - Search Marketing & TechWe're looking for a Senior Machine Learning Scientist to provide technical leadership within our Search Marketing & Tech organization at Expedia Group. This role is for someone who has demonstrated a track record of delivering high-impact ML projects from concept through production, partnering closely with engineering teams on multi-quarter initiatives that drive measurable business outcomes.

Our team builds and optimizes the ML models that power metasearch bidding and auction strategies across key partners (Google Hotel Ads, Trivago, Tripadvisor). As a senior technical leader, you will own end-to-end ML solutions for a domain area, define the technical roadmap, and drive the execution of complex projects that improve customer experiences and business performance at scale.

In this role, you will: Technical Leadership & OwnershipOwn end-to-end ML solutions within your domain, from problem framing and metric design through data exploration, model development, deployment, and post-launch iterationDefine technical direction for your area, including model architecture, system design, data contracts, and integration patterns with existing servicesLead multi-quarter ML initiatives in partnership with engineering, product, and business stakeholders, driving projects from ambiguous requirements to production systems at scaleAuthor technical blueprints and system designs that clearly outline objectives, constraints and trade-offs for complex ML systemsModel Development & ProductionDesign and implement production-grade ML models (e.g., gradient-boosted trees, deep learning, optimization algorithms, bandits/RL policies) that operate reliably under real-world constraints in collaboration with engineering.

Build robust training, evaluation, and serving pipelines with embedded observability, drift detection, and failure handling across the ML lifecycleEnhance experimentation and measurement strategies, including A/B tests, causal inference methods, and long-horizon metrics to ensure models deliver durable impact as data and user behavior evolveCross-Functional Collaboration & InfluencePartner with engineering teams to translate ML designs into scalable, maintainable production systems, ensuring alignment on timelines, dependencies, and technical standardsInfluence domain roadmaps by connecting ML opportunities to business objectives, articulating trade-offs, and building stakeholder alignment through evidence-based recommendationsTranslate ambiguous business problems into clear ML formulations with measurable success criteria, balancing technical feasibility with business impactLead structured reviews with cross-functional partners, presenting complex technical concepts and trade-offs to both technical and non-technical audiencesStandards, Mentorship & Team DevelopmentRaise the technical bar for the broader science community by codifying best practices, experimentation standards, and reusable patternsMentor other data and machine learning scientists, providing technical guidance through code reviews, design discussions, and knowledge sharingDrive adoption of AI best practicesExperience & Qualifications: Master's or PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, or related quantitative field, or equivalent industry experience6+ years (Master's) or 4+ years (PhD) of hands-on experience applying machine learning to real-world problemsDemonstrated track record of leading at least one complex, multi-stakeholder production ML initiative that delivered measurable business impactTechnical DepthDeep ML expertise in supervised and unsupervised learning, including tree-based methods, generalized linear models, and/or deep learning, with strength in feature engineering, regularization, calibration, and error analysisStrong experimentation and statistics skills: designing and interpreting A/B tests, understanding bias/variance and statistical power, and applying causal inference techniques (e.g., diff-in-diff, IV, matching) where randomization is impracticalFluency in Python and core data/ML libraries (pandas, NumPy, scikit-learn, PyTorch or TensorFlow), combined with solid software engineering practices (clean code, testing, version control, code review)Proficient with large-scale data: strong SQL skills and familiarity with distributed data processing (e.g., Spark, Hive) for building training datasets, features, and analytical viewsLeadership & CollaborationProven ability to lead through influence: aligning cross-functional stakeholders on problem definitions, success metrics, and rollout plans across multi-quarter projectsStrong communication skills: articulating technical concepts, trade-offs, and recommendations clearly to both technical and non-technical audiencesExperience with complex system diagnosis: combining logs, metrics, experiments, and domain intuition to identify root causes and drive data-informed remediation plansPreferred QualificationsExperience with ads, auctions, marketplace optimization, or bidding systems (e.g., CPC/CPA bidding, budget pacing, ranking, ROI optimization, Controllers)Familiarity with multi-objective or constrained optimization problems, balancing competing objectives (e.g., profit, volume, ROI) using modeling, heuristics, or RL/bandit methodsHands-on experience with modern ML production practices: feature stores, model registries, CI/CD for ML, automated monitoring and alertingExperience shaping team-level technical direction: proposing and prioritizing ML investments, identifying reusable components, and defining standards for experimentation and documentationExposure to causal inference or advanced experimentation techniques in noisy business environments (e.g., geo-based tests, synthetic controls, uplift modeling)Experience with AI/ML-driven systems, including exposure to large language models or foundation model fine-tuning and evaluationAccommodation requestsExpedia Group is committed to providing an inclusive and accessible recruiting experience.

If you need an accommodation or adjustment due to a disability during the application or recruiting process, please submit a request at .About Expedia GroupExpedia Group includes three flagship consumer brands - Expedia, Hotels.com, and Vrbo - along with a leading B2B travel business and travel advertising offerings. Across our brands and business, we help travelers explore the world with confidence and ease.

Important noticeEmployment opportunities and job offers at Expedia Group will always come from Expedia Group's Talent Acquisition and hiring teams. Never share sensitive personal information unless you are confident of the recipient. Expedia Group does not extend job offers via email or messaging tools to individuals with whom we have not made prior contact. Our email domain is @expediagroup.com. The official place to find and apply for roles is .Equal OpportunityExpedia is committed to creating an inclusive work environment with a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, gender, sexual orientation, national origin, disability or age.

SummaryLocation: UK - LondonType: Full time