Senior Manager, Machine Learning Science - Bundled Products Recommendations

Expedia•Seattle, WA

23h

About The Position

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. The Whole Trip AI team at Expedia Group enables unforgettable travel experiences. The team’s responsibilities cover search ranking & recommendations for Brand Expedia across core lines of business including Flights, Cars, Packages, and Activities. Additionally, we’re responsible for optimizing our interactions with travelers across this domain, including cache optimization, pricing forecasting, and next-best action modeling. Our approaches include both traditional ML as well as GenAI-based solutions. We work closely with other teams in the Data & AI organization to continually improve our tools, processes, and platforms for building and deploying industry leading AI solutions. As a Senior Manager, Machine Learning Science you and your team will own our Bundled products ranking, recommendations, and intent modeling. You will work closely with stakeholders in Cars, Activities, Attach & Packages to help travelers book multi-item trips with Expedia by surfacing relevant offerings in the right places in the experience. Your team owns multiple high traffic, live models and with significant scope for further growth. You will work with a dynamic group of product managers, engineers, and scientists to achieve Expedia’s business goals.

Requirements

Bachelor’s Degree or Equivalent Level; Technical Degree Preferred
8+ years of relevant professional experience and 3+ years of people management experience
Strong expertise in modern machine learning methods in ranking and recommendation with solid programming skills and collaboration experience with engineering teams on system, API, and data design
Substantial professional experience leading end-to-end machine learning initiatives, including problem definition, data preparation, feature engineering, model development, offline evaluation, experimentation, and integration into production systems
Proven experience managing or technically leading machine learning scientists or applied researchers, with ownership spanning multiple services, products, or problem spaces

Nice To Haves

Experience with natural language search and refinement of recommendations using natural language
Experience with multi-modal recommendations across images, structured metadata, and unstructured text
Demonstrated track record delivering large-scale machine learning solutions in high-traffic, data-rich environments, including setting technical direction and architecture for multi-service or multi-domain ML systems
Experience designing and operationalizing experimentation platforms, advanced measurement frameworks, or causal inference methodologies to drive data-informed decision making at scale
Leadership experience in raising the technical bar for ML teams through publication-quality analyses, reusable modeling frameworks, rigorous data and model quality practices, and effective documentation
Proven ability to advance a team’s capabilities with emerging AI/ML techniques and tooling, including GenAI & agentic techniques, to unlock new product experiences and operational efficiencies

Responsibilities

Enable a team to develop industry-leading ranking & recommendation models for the travel industry to enable travelers to have memorable travel experiences
Lead and grow a team of machine learning scientists to deliver production-grade ML solutions that solve complex business problems and improve traveler and partner experiences across multiple product domains
Define and drive the end-to-end machine learning roadmap for your area, from problem formulation and data strategy through model design, evaluation, and productionization in close partnership with engineering and product
Set and enforce best practices for experimental design, A/B testing, and causal inference to ensure ML-driven decisions are statistically robust, interpretable, and aligned to business and customer outcomes
Partner with data engineering and software engineering teams to ensure ML models are scalable, resilient, observable, and well-integrated into services, APIs, and data pipelines in production
Mentor, coach, and develop ML scientists through technical guidance, peer review, and career development, fostering a culture of scientific rigor, continuous learning, and high impact delivery
Apply familiarity with AI-driven systems, tools, or workflows and AI/ML concepts to real world products, safely integrating and operating AI/ ML ‑ enabled solutions that improve outcomes across multiple lines of business