Machine Learning Scientist III

Expedia Group•San Jose, CA

3d•$137,500 - $238,500

About The Position

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. Introduction to team: Are you passionate about using machine learning to improve Customer Experience? Would you like to work in the fast-paced, competitive, customer-focused, and data-rich world of online travel? Our Machine Learning and Data Science team are growing! We are looking to hire researchers and data scientists interested in breaking new ground to tackle some of the most complex customer experience problems in the travel domain. The focus of your job will be on developing state-of-the-art machine learning algorithms to power and enhance the customer experience across highly complex post-booking recommendations, customer service, and trip management use cases. You will tackle substantial technical challenges, from inference problems arising from long-tail traveler data to multi-objective optimization problems in the highly dynamic, operationally complex environment of customer service. Your passion for the craft of ML and AI will unlock tangible growth for our business by exploiting these rich data sets and building effective solutions for travelers and our partners. This is your opportunity to build the core algorithms that help Expedia Group's Post Booking organization bring context and intelligence to every step of the traveler journey and redefine what service excellence in travel can be. We are looking for a hands-on scientist who is passionate about applying machine learning to complex prediction and optimization problems that drive an ecosystem that anticipates traveler needs, personalizes dynamic add-ons and upsells, and improves service experiences, making travel more seamless for millions of customers and partners worldwide.

Requirements

PhD or MS in a quantitative field (e.g., Computer Science, Economics, Statistics, Physics).
3+ years of hands-on industry experience building and deploying machine learning models to solve real-world problems.
Expertise in applied ML: Deep, practical knowledge of machine learning theory (supervised/unsupervised learning, deep learning) and statistical modeling and a strong command of experimental design (A/B testing) and causal inference to accurately measure impact.
Able to design end-to-end ML solutions: framing the problem, choosing data sources, selecting algorithms, and defining evaluation strategy.
Experience with the machine learning software development lifecycle, including experimentation, deployment, monitoring, and iteration in production.
Strong programming skills in at least one major ML language (e.g., Python, Scala, Java) plus SQL; writes clean, modular, maintainable code.
Technical Fluency: Strong programming skills in Python and its data science ecosystem (e.g., pandas, scikit-learn, pySpark), plus proficiency in SQL.
Follow software engineering best practices and contribute to the team's shared codebase.
First-Principles Problem Solver: Skilled at dissecting ambiguous problems and clearly communicating complex technical ideas.
Minimum Qualifications: Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience.
5+ years of relevant professional experience.
Proven ability to design end-to-end ML solutions, including problem formulation, identification and preparation of data sources, algorithm selection, feature engineering, evaluation strategy, and production deployment and monitoring.
Strong programming skills in Python and its data science ecosystem (such as pandas, scikit-learn, PySpark) and proficiency in SQL, with experience following software engineering best practices and contributing to shared codebases.
Familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products, including experience with the machine learning software development lifecycle from experimentation through operational monitoring.

Nice To Haves

Domain knowledge in customer service, recommendation systems, operational applications of ML, and/or e-commerce
Experience with reinforcement learning or other advanced ML techniques is a plus
Experience building and deploying models using GenAI/LLM technologies
Experience translating research and academic papers into improved model designs and techniques
Preferred Qualifications: MS or PhD in a quantitative field such as Computer Science, Economics, Statistics, Physics, or a related discipline.
3+ years of hands-on industry experience building, deploying, and iterating on machine learning models that solve real-world problems in production environments.
Proven ability to design end-to-end ML solutions, including problem formulation, identification and preparation of data sources, algorithm selection, feature engineering, evaluation strategy, and production deployment and monitoring.
Strong programming skills in Python and its data science ecosystem (such as pandas, scikit-learn, PySpark) and proficiency in SQL, with experience following software engineering best practices and contributing to shared codebases.
Familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products, including experience with the machine learning software development lifecycle from experimentation through operational monitoring.

Responsibilities

Design & Implement ML Solutions: Take ownership of the end-to-end ML lifecycle for your projects, from ideation and research to deployment and monitoring.
Test, Learn, and Iterate: Design and analyze tests to validate your models and quantify their business impact and design future iterations.
Collaborate and Communicate: Partner closely with product managers, engineers, and business stakeholders to understand requirements, define problems, and communicate your findings and results effectively.