Introduction to the team

TheWhole TripAIteam at Expedia Group enables unforgettable travel experiences. The team 27s responsibilities cover 25search 27 ranking 26 recommendations for Brand Expedia across core 27lines of business 27including 25Flights, Cars, Packages,and Activities. Additionally, we 27reresponsible for 25optimizing 25our interactions with travelers across this domain, including cache optimization, pricing forecasting, and next-best action modeling. Our 27approaches include 25both 25traditional ML 25as well as 25GenAI-based solutions. 25We work closely with other teams in the Data 26 AI organization to continually improve our tools, processes, and platforms for building and deploying industry leading 25AI 25solutions. 25

As a Senior Manager, Machine Learning 25Science 25you and your team will own our Bundled Products 25ranking, recommendations, and intent modeling. 25You will work closely with stakeholders in 25Cars, Activities, 25Attach 26 25Packages to help 25travelers book multi-item trips with Expedia 25by 25surfacing 25relevant offerings in the right places in the experience. Your team owns multiple high traffic, live models and with significant scope for further growth. 25You will work with a dynamic group of 25product managers, 25engineers, and scientists 25to achieve Expedia 27s business goals. 25

In this role, you will:

• Enable a team to developindustry-leading ranking 26 recommendation models for 25the 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, andproductionizationin 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 25enabledsolutions that improve outcomes across multiple lines of business

Minimum Qualifications:

• Bachelor 27s Degree or Equivalent Level; Technical Degree Preferred

• 8+ years of relevant professional experience and 3+ yearsof peoplemanagement experience

• Strongexpertisein 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

Preferred Qualifications:

• Experience with natural language search and refinement of recommendations using natural language

• Experience with multi-modal recommendations across images, structured metadata, and unstructured text

• Demonstratedtrack recorddelivering 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 makingatscale

• 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 27s capabilities with emerging AI/ML techniques and tooling, includingGenAI 26 agentic techniques, 25to unlock new product experiences and operational efficiencies