Principal ML Scientist

Expedia•Seattle, WA

93d

About The Position

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. We are looking for a highly influential science leader to help drive the technical strategy and execution for LLM-powered AI Agent servicing for Expedia travelers and partners, with strong traditional ML based personalization skills along with Agentic AI skillsets within the servicing domain. This role will define and lead the transformation towards scalable, voice and chat based self-service experiences powered by AI agents—reducing customer effort while improving resolution speed, consistency, customer satisfaction, and operational efficiency. As a Principal Machine Learning Scientist, you will partner with key senior leaders to architect the long-term science vision for customer AI servicing, drive complex cross-functional execution, and build a culture of scientific thinking, research and innovation in the organization. You will operate as an indispensable leader, shaping how agentic AI is designed, governed, measured, and scaled across TPSP.

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.

Nice To Haves

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 Marketplace 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).

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.