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.
Why Join Us?
To shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and know that when one of us wins, we all win.
We provide a full benefits package, including exciting travel perks, generous time-off, parental leave, a global hybrid work setup (with some pretty cool offices), and career development resources, all to fuel our employees' passion for travel and ensure a rewarding career journey. We’re building a more open world. Join us.
Introduction to the Team
This role sits centrally within the Product & Technology division but with a focus on building ML solutions to power Expedia Group's B2B Business. You will join a team that builds end-to-end ML solutions for ranking problems, recommendation engines as well as optimizing our pricing and commission offerings contributing substantial value for our partners and for EG. We embrace test and learn by continuously experimenting, analyzing and improving our algorithms which has helped the B2B business become one of the fastest growing at Expedia Group.
Private Label Solutions is the B2B arm of Expedia Group. We open up our supply and innovative technology to businesses looking to take on the world of travel. These businesses, sometimes referred to as our ‘demand partners’, include global financial institutions (e.g. AMEX), corporate managed travel, offline travel agents (e.g. Flight Centre), global travel suppliers (e.g. Delta) and many more.
What you will do:
* Applying statistics methods like confidence intervals, point estimates and sample size estimates to make sound and confident inferences on data and A/B tests.
* 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.
* Modeling rich and complex online travel data to understand, predict and optimize business metrics to help improve the partner experience.
* Framing business problems as data science problems with a concrete set of tasks.
* Collaborate with technology and business divisions as appropriate.
* Articulate solutions, methodologies and frameworks concisely to both technical and non-technical partners.
Who you are:
* You have a Bachelors or Master's 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 and Computer Engineering or equivalent experience.
* You have a base knowledge of ML techniques like Regression, Naïve Bayes, Gradient Boosting, Random Forests, SVMs, Neural Networks, and NLP.
* You have some experience with a programming, statistical, and/or querying languages like Python, R, SQL/Hive, Java.
* You understand distributed file systems, scalable datastores, distributed computing and related technologies (Spark, Hadoop, etc.); implementation experience of MapReduce techniques, in-memory data processing, etc.
* You’re familiar with cloud computing, AWS specifically, in a distributed computing context.
* You’re able to effectively communicate and engage with a variety of partners (e.g., internal, external, technical, non-technical people).
Accommodation requests
If you need assistance with any part of the application or recruiting process due to a disability, or other physical or mental health conditions, please reach out to our Recruiting Accommodations Team through the Accommodation Request.
We are proud to be named as a Best Place to Work on Glassdoor in 2024 and be recognized for award-winning culture by organizations like Forbes, TIME, Disability:IN, and others.
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