Machine Learning Science Graduate - Masters - 2026 - San Jose, Seattle

Expedia•San Jose, CA

106d•Hybrid

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. In this role, you will make an immediate impact on reimagining the way people search for travel by inventing brand-new techniques to power global travel. You will be involved in building pipelines, prototyping new ML models with A/B testing, and applying new techniques to services that run tens of thousands of requests per second. There are numerous opportunities for technical innovation at Expedia Group.

Requirements

Must be available to start on August 17, 2026
Must be graduating between December 2025 and July 2026 with a Masters degree in a technical, or analytical-related, subject such as Computer Science (with focus in areas like Artificial Intelligence, Machine Learning, Natural Language Processing, Data Mining, Data Science), Mathematics, Physics, Statistics, Operations Research, Electrical & Computer Engineering
Must be willing to relocate to city of job location if outside commuting distance

Nice To Haves

Helpful to understand ML techniques like Regression, Naïve Bayes, Gradient Boosting, Random Forests, SVMs, Neural Networks, and NLP
Helpful to have experience with programming, statistical, and querying languages like Python, R, SQL/Hive, Java
Helpful to understand distributed file systems, scalable datastores, distributed computing and related technologies (Spark, Hadoop, etc.); implementation experience of MapReduce techniques, in-memory data processing, etc.
Helpful to be familiar with cloud computing, AWS specifically, in a distributed computing context
Helpful to be able to effectively communicate and engage with a variety of partners (e.g., internal, external, technical, non-technical people)

Responsibilities

Applying statistics methods like confidence intervals, point estimates and sample size estimates to make sound and confident inferences on data and A/B tests
Applying Natural Language models to Google keyword analysis and applying meta models to our multi-objective ranking problem
Communicating complex analytical topics in a clean & simple way to multiple partners and senior leadership (both internal & external)
Conducting feature engineering and modifying existing models/techniques to suit business needs
Developing domain expertise in fraud & risk to understand how to detect risky transactions
Modeling rich and complex online travel data to understand, predict and optimize business metrics to help improve the traveler experience
Framing business problems as data science problems with a concrete set of tasks
Apply your domain (i.e. travel, online retail) knowledge, business acumen (understanding the underlying business objectives), and critical reasoning skills to your work