Principal ML Scientist

About the role

This role sits within Expedia’s Traveler & Partner Service Platform (TPSP) Product & Technology organization, which builds the core capabilities and experiences that power customer service across the Expedia ecosystem. TPSP enables both travelers and partners to receive high-quality, efficient service through a combination of human support and AI-powered experiences.

Responsibilities

• Define and lead the ML strategy for one or more complex product or platform areas (e.g., personalization, search & recommendations, pricing, fraud/risk, or generative AI–powered experiences).
• Formulate ill-defined business problems into well-posed ML problems, selecting appropriate modeling approaches and success metrics.
• Design and implement state-of-the-art ML models (e.g., deep learning, representation learning, causal inference, bandits, LLM/GenAI where appropriate) and own them through production.
• Partner closely with engineering to productionize models, improve data and feature pipelines, and ensure reliability, latency, and scalability requirements are met.
• Lead experiment design and evaluation, including A/B tests and offline evaluations; drive decisions with rigorous statistical analysis.
• Proactively identify opportunities where ML can unlock new customer and business value, build business cases, and influence product and leadership roadmaps.
• Establish and champion best practices for model development, evaluation, monitoring, and responsible AI (fairness, robustness, privacy, and safety).
• Provide technical leadership and mentorship to other ML scientists, data scientists, and ML engineers through design reviews, pairing, and informal coaching.
• Communicate complex technical topics in clear, concise ways to executives, product leaders, and non-technical stakeholders, influencing direction across teams and organizations.
• Collaborate with platform and infra teams to evolve ML tools, feature stores, experimentation platforms, and model monitoring capabilities.

Requirements

• Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, or a related technical field; or equivalent practical experience.
• 10+ years of experience in applied ML / data science, including significant experience owning production models in complex domains.
• Deep expertise in machine learning algorithms and statistical modeling, such as gradient-boosted trees, deep learning, representation learning, or similar.
• Strong Python skills and experience with major ML libraries and frameworks (e.g., PyTorch, TensorFlow, JAX, scikit-learn), and data tools (e.g., Spark, SQL).
• Proven track record of shipping production ML systems at scale and driving measurable product or business impact.
• Experience designing and interpreting experiments (A/B tests), with solid grounding in statistics (estimation, hypothesis testing, causal inference basics).
• Demonstrated ability to lead cross-functional initiatives with product, engineering, and business partners, without formal authority.
• Excellent communication skills, with the ability to explain complex ML concepts to diverse audiences and drive alignment.

Preferred Qualifications

• Advanced degree (MS or PhD) in Computer Science, Machine Learning, Statistics, Operations Research, or a related quantitative field.
• Experience in one or more relevant domains, such as: Personalization, search & recommendations, ranking, or relevance Marketplaces optimization, pricing, or yield management Fraud/risk modeling, anomaly detection Natural language processing / LLMs / generative AI Experience architecting end-to-end ML systems: data/feature pipelines, model training, evaluation, deployment, and monitoring in distributed environments.
• Demonstrated impact in technical leadership: setting standards, influencing roadmaps across teams, and mentoring senior ICs.
• Familiarity with MLOps practices and platforms (feature stores, CI/CD for models, model monitoring & alerting).
• Publications in top-tier ML/AI conferences or journals, or equivalent evidence of thought leadership (talks, patents, open-source).