Lead Machine Learning Engineer– NIKE USA Inc., Beaverton, OR. Develop robust advanced analytics and machine learning solutions that have a direct impact on the business; own projects end-to-end - from conception to operationalization, demonstrating an understanding of the full software development lifecycle; provide technical vision and guidance to the teammates; work with the Artificial Intelligence and Machine Learning (Al/ML) team at Nike with teammates in Portland, Boston, and Poland, and join a global organization working to solve machine learning problems at scale; design and implement scalable applications that leverage prediction models and optimization programs to deliver data driven decisions that result in immense business impact; contribute to core advanced analytics and machine learning platforms and tools to enable both prediction and optimization model development; develop and share new skills, mentor, and contribute knowledge and software back to the analytics and engineering communities both within Nike and at-large. Telecommuting is available from anywhere in the U.S., except from SD, VT, and WV. Must have a Master’s degree in Information Systems, Computer Science and 2 years of experience in the job offered or in a engineering-related occupation. Position requires: • Python • AWS (Amazon Web Services) • Machine Learning / MLOps • Docker • CI/CD (Jenkins) • SQL and ETL Pipelines • Data Structures, Data Modeling and Software Architecture • Apache Spark • Kubernetes • Databricks • Terraform • Database Technologies (Snowflake, PostgreSQL) Apply at www.jobs.nike.com (Job# R-95900) #LI-DNI We offer a number of accommodations to complete our interview process including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed. If you discover, as you navigate our application process, that you need assistance or an accommodation due to a disability, please complete the Candidate Accommodation Request Form.