Machine Learning Scientist II
1 month ago
Machine Learning Scientist II
This role sits centrally within the Product & Technology division but with a focus on building ML solutions to power Expedia Groups 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.
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:
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.
Must have a base knowledge of ML techniques like Regression, Naïve Bayes, Gradient Boosting, Random Forests, SVMs, Neural Networks, and NLP
Must have some experience with a programming, statistical, and/or 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)
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