Machine Learning Scientist III
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 flexible work model (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
We are seeking a skilled Machine Learning Scientist III to join Expedia Group B2B. As a global leader in B2B travel technology, PLS serves thousands of partners across diverse markets and travel segments. Recommender systems and relevance algorithms are foundational capabilities that have a significant impact on the business and are critical to the success of our partners, suppliers and Expedia Group. This applied science role will contribute to developing and implementing ML solutions for personalised recommendations, learning to rank, and relevance optimisation. The ideal candidate will possess strong technical skills and commercial awareness to drive value for PLS and our partners.
In this role, you will:
* Own the design and implementation of end-to-end ML solutions for recommendations & relevance at scale that can handle high-throughput, low-latency personalized recommendations across diverse partner segments
* Collaborate with senior team members and cross-functional teams to align ML solution design with business strategy and partner needs
* Be an integral part of a full stack team of Machine Learning Scientists and Machine Learning Engineers, contributing to technical implementation, conduct analyses and present findings to both technical and business stakeholders, translating ML concepts into actionable insights
* Work with operations, analytics and internal product & technology teams to ensure models in production are driving expected business value and operate efficiently
* Stay informed about relevant advancements in recommender systems and relevance algorithms through ongoing research and learning
* Coach and mentor other scientists and engineers within the recommendations domain
Experience and qualifications:
* You hold a PhD (preferred) or master’s degree in computer science, machine learning, mathematics/statistics, or another related field of science, with a minimum of 3+ years of industry experience in applied machine learning, including deploying recommender systems and relevance models to production.
* You have solid knowledge of machine learning algorithms, particularly those used in recommender systems and learning to rank, as well as knowledge of statistics and A/B testing.
* You demonstrate good communication and interpersonal skills, with the ability to work effectively in a team environment and contribute to technical discussions.
* You show interest in staying current with the latest ML research and applying techniques to solve real-world problems in recommendation and relevance.
* You are proficient in programming languages such as Python, and have experience with ML frameworks like TensorFlow, PyTorch, and scikit-learn.
* You are familiar with cloud platforms (e.g., AWS), big data technologies (e.g., Spark), and technologies used to deploy models to production (e.g., Docker).
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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