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Machine Learning Scientist II
3 months ago
Machine Learning Scientist II
This position as a Machine Learning Scientist II plays a key role in the organization, providing a fresh outlook and a wide range of challenges.
We develop comprehensive solutions to enhance pricing algorithms independently, delivering significant value to PLS and our collaborators.
We are avid supporters of the test and learn approach, constantly experimenting, analyzing, and enhancing our algorithms, leading PLS to become the fastest-growing entity within Expedia Group.
Responsibilities:
- Implementing statistical methods such as confidence intervals, point estimates, and sample size estimates to draw solid and assured conclusions on data and A/B tests
- Creating pricing algorithms and setting up the in-house machine learning systems
- Conveying intricate analytical concepts in a clear and straightforward manner to various partners and senior management (both internal & external)
- Performing feature engineering and adapting existing models/techniques to meet business requirements
- Analyzing rich and intricate online travel data to comprehend, predict, and enhance business metrics to enrich the partner experience
- Defining business challenges as data science issues with a specific set of objectives
- Collaborating with technology and business units as necessary
- Articulating solutions, methodologies, and frameworks succinctly to both technical and non-technical stakeholders
Qualifications:
- Bachelor's or Master's degree in a technical or analytical-related field such as Computer Science (specializing 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 practical knowledge.
- Proficient in ML techniques like Regression, Naïve Bayes, Gradient Boosting, Random Forests, SVMs, Neural Networks, and NLP
- Familiarity with programming, statistical, and/or querying languages like Python, R, SQL/Hive, Java
- Understanding distributed file systems, scalable datastores, distributed computing, and associated technologies (Spark, Hadoop, etc.); hands-on experience with MapReduce techniques, in-memory data processing, etc.
- Acquainted with TensorFlow and cloud computing (particularly AWS, in a distributed computing context)
- Ability to effectively interact and collaborate with various partners (e.g., internal, external, technical, non-technical individuals)
