Become a Part of the NIKE, Inc. Team
NIKE, Inc. does more than outfit the world’s best athletes. It is a place to explore potential, obliterate boundaries and push out the edges of what can be. The company looks for people who can grow, think, dream and create. Its culture thrives by embracing diversity and rewarding imagination. The brand seeks achievers, leaders and visionaries. At NIKE, Inc. it’s about each person bringing skills and passion to a challenging and constantly evolving game.
Lead Data Scientist- Nike Inc.- Beaverton, OR. Work closely with Data Science and Engineering teammates to build, support, and enable end-to-end personalized shopping experience use cases. Build credibility for algorithmic approaches through demonstrating functional expertise and experience. Innovating to drive more temporally precise engagement, and broader adoption across the organization. Establish and support large-scale measurement and optimization of consumer experiences for different geographies in a production/testing environment. Enhance and consult on best Search and Recommender System practices to connect the impact of marketplace drivers and business short-term and long-term outcomes. Partner with peers in both Data Science and Engineering, developing scalable data, improving current models, systems, and modeling processes. Utilize common tools and instruments to help establish operational excellence for Nike. Drive the application and implementation of innovative best-in-class methods and tools to derive useful insights for a wide variety of business goals in support of merchandising efforts. Stay up to date on industry trends, tools, and platform capabilities relevant to the team’s disciplines. Telecommuting is available from anywhere in the U.S., except from AK, AL, AR, DE, HI, IA, ID, IN, KS, KY, LA, MT, ND, NE, NH, NM, NV, OH, OK, RI, SD, VT, WV, and WY.
Must have a Master's degree in Computer Science, Data Science and two (2) years of experience in the job offered or data-related occupation. Experience must include the following:
- Building and maintaining predictive and/or prescriptive models in production at enterprise scale
- Synthesizing and packaging complex analyses and delivering results to non-technical audiences including executive leadership teams
- Developing and running simulation and optimization tools to support decision making
- Python and SQL
- Machine Learning (Regression, Classification, Clustering) and Deep Learning(MLP, LSTM, CNN);
- Time Series Modeling
- Design of Experiments
- Cloud Services (Amazon Web Services)
- Spark and Hadoop
- Data Visualization (Tableau)
- Scheduling Tools (Airflow)
- Agile Development Process
- Quality Assurance Testing and Automation
Apply at www.Nike.com/Careers (Job# R-61369)
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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.
OUR HIRING GAME PLAN
01 Apply
Our teams are made up of diverse skillsets, knowledge bases, inputs, ideas and backgrounds. We want you to find your fit – review job descriptions, departments and teams to discover the role for you.
02 Meet a Recruiter or Take an Assessment
If selected for a corporate role, a recruiter will reach out to start your interview process and be your main contact throughout the process. For retail roles, you’ll complete an interactive assessment that includes a chat and quizzes and takes about 10-20 minutes to complete. No matter the role, we want to learn about you – the whole you – so don’t shy away from how you approach world-class service and what makes you unique.
03 Interview
Go into this stage confident by doing your research, understanding what we are looking for and being prepared for questions that are set up to learn more about you, and your background.
