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 & Tech
We’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 & 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, 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 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 behavior 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, generalized linear models, and/or deep learning, with strength in feature engineering, regularization, 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 randomization 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