Machine Learning Specialist

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 Scientist - Search Marketing & Tech

We’re looking for a Senior Machine Learning Scientist to provide technical leadership within our Search Marketing & Tech organisation 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 optimises 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 & Ownership

• Own end-to-end ML solutions within your domain, from problem framing and metric design through data exploration, model development, deployment, and post-launch iteration
• Define technical direction for your area, including model architecture, system design, data contracts, and integration patterns with existing services
• Lead multi-quarter ML initiatives in partnership with engineering, product, and business stakeholders, driving projects from ambiguous requirements to production systems at scale
• Author technical blueprints and system designs that clearly outline objectives, constraints and trade-offs for complex ML systems

Model Development & Production

• Design and implement production-grade ML models (e.g., gradient-boosted trees, deep learning, optimisation 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 lifecycle
• Enhance 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 behaviour evolve

Cross-Functional Collaboration & Influence

• Partner with engineering teams to translate ML designs into scalable, maintainable production systems, ensuring alignment on timelines, dependencies, and technical standards
• Influence domain roadmaps by connecting ML opportunities to business objectives, articulating trade-offs, and building stakeholder alignment through evidence-based recommendations
• Translate ambiguous business problems into clear ML formulations with measurable success criteria, balancing technical feasibility with business impact
• Lead structured reviews with cross-functional partners, presenting complex technical concepts and trade-offs to both technical and non-technical audiences

Standards, Mentorship & Team Development

• Raise the technical bar for the broader science community by codifying best practices, experimentation standards, and reusable patterns
• Mentor other data and machine learning scientists, providing technical guidance through code reviews, design discussions, and knowledge sharing
• Drive adoption of AI best practices

Experience & Qualifications:

• Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, or related quantitative field, or equivalent industry experience
• 6+ years (Master’s) or 4+ years (PhD) of hands-on experience applying machine learning to real-world problems
• Demonstrated track record of leading at least one complex, multi-stakeholder production ML initiative that delivered measurable business impact

Technical Depth

• Deep ML expertise in supervised and unsupervised learning, including tree-based methods, generalised linear models, and/or deep learning, with strength in feature engineering, regularisation, calibration, and error analysis
• Strong 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 randomisation is impractical
• Fluency 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 views

Leadership & Collaboration

• Proven ability to lead through influence: aligning cross-functional stakeholders on problem definitions, success metrics, and rollout plans across multi-quarter projects
• Strong communication skills: articulating technical concepts, trade-offs, and recommendations clearly to both technical and non-technical audiences
• Experience with complex system diagnosis: combining logs, metrics, experiments, and domain intuition to identify root causes and drive data-informed remediation plans

Preferred Qualifications

• Experience with ads, auctions, marketplace optimisation, or bidding systems (e.g., CPC/CPA bidding, budget pacing, ranking, ROI optimisation, Controllers)
• Familiarity with multi-objective or constrained optimisation problems, balancing competing objectives (e.g., profit, volume, ROI) using modelling, heuristics, or RL/bandit methods
• Hands-on experience with modern ML production practices: feature stores, model registries, CI/CD for ML, automated monitoring and alerting
• Experience shaping team-level technical direction: proposing and prioritising ML investments, identifying reusable components, and defining standards for experimentation and documentation
• Exposure to causal inference or advanced experimentation techniques in noisy business environments (e.g., geo-based tests, synthetic controls, uplift modelling)
• Experience with AI/ML-driven systems, including exposure to large language models or foundation model fine-tuning and evaluation.