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Principal Data Scientist

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Technology
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IR323 Requisition #
NIKE, Inc., Beaverton, OR. Will build machine learning models to maximize user engagement with communications and marketing messages in Beaverton, OR. Will partner with personalization data scientists, data engineers, data analysts, operations teams, and product teams. Will apply machine learning and statistical algorithms in the practice of delivering communications to members. Will establish best practices for leveraging outputs of machine learning algorithms in the selection and delivery of communications to members. Will prototype and pilot audience capabilities related to the optimization of communications. Will establish testing plans, success criteria, and integration patterns for communications algorithms. Will partner with product and engineering teams in an embedded squad model to deliver requirements for the incorporation of modeling capabilities into available marketing tools. Will create presentations of modeling capabilities for non-technical partners of various backgrounds. Will collaborate with the broader data science community. Will serve as a representative at conferences and community events. Will deploy models in a production environment and provide expertise in evaluating models and providing feedback to model development. Will work within reinforcement learning and deep learning models. Will use contextual bandit algorithms. 
Education: Bachelor’s degree in computer science, math, statistics, physics, economics, data science, or information management. Will also accept a Master’s degree in computer science, math, statistics, physics, economics, data science, or information management. Experience: A bachelors degree and five years of overall progressive experience in data science. Will also accept a Master’s degree and three years of experience in data science. Skills/Requirements6 months of experience: (1) building machine learning or deep learning algorithms; (2) deploying models into a production environment; (3) evaluating machine learning, deep learning, or statistical models; (4) end to end deployment and building data pipelines; (5) coordinating cross functionally to develop and deploy models; and (6) preparing and presenting technical information to technical and non-technical audiences. 

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