Serve as an integral member of a multi-functional engineering team that delivers solutions unlocking machine learning for Nike. You will analyze and profile data to uncover insights in support of scalable solutions, and clean, prepare and verify the integrity of data for analysis and model creation. You’ll also track model accuracy, performance, relevance, and reliability. You will apply a variety of machine learning and collaborative filtering methods to data sets. You will aid in building APIs and software libraries that support the adoption of models in production
Leverage your prior experience, knowledge of industry trends, and personal creativity to develop new and innovative solutions which delight our customers in their mission to serve Athletes
Stay current with industry trends and recommend relevant technologies & products in the areas of Analytics, Machine Learning, Artificial Intelligence, and Data Science tools and other emerging technologies. Given the rapid pace of change in technology and machine learning today, always be pushing the boundary of what’s possible and be on the offence always
Accept and embody Nike’s core values (maxims) in your work and interactions with peers and stakeholders. Communicate effectively, build trust and strong relationships across the company, and do the right thing
Requirements & Skills:
3+ years of experience in the field of ML Engineering or Software Engineering with a comparable bachelor’s degree in a relevant field, like computer science or software engineering, or equivalent experience.
Understanding of Machine Learning, its applications, and the lifecycle of an ML application in production; including an ability to articulate the role of MLOps in the machine learning development life cycle from experimentation to production and measurement
An ability to significantly communicate, written, orally, and visually technical topics with peers and articulate the benefits and tradeoffs of various solutions
Ability to write robust, maintainable, and extendable code in Python
Experience working in and/or collaborating with a partial or fully distributed team
Strong experiential understanding of data structures, algorithms, and data solutions
Familiarity with frameworks such as Scikit-learn, PyTorch, Tensorflow, Spark, FastAPI or similar platforms and frameworks
Experience with cloud architecture and technologies (preferably Amazon Web Services: ECR, SageMaker, Lambda, API Gateway)
Familiarity with pipeline orchestration tools such as AirFlow.
Awareness of CI/CD pipelines and containerization is preferred.
Fluency in the application of open-source technologies and the potential of standardized platforms in the area of Data Science, AI & ML.
Nice to have: an interest in the potential of Generative AI to accelerate common development and data science tasks, or the deployment of Generative AI solutions in the enterprise
