Serve as part of a cross-functional leadership team to grow highly skilled, diverse engineering teams that deliver solutions that unlock machine learning for Nike. Engage employees, assist the teams by removing roadblocks, and align efforts across Nike’s matrix to drive progress forward and always win as a team.
Leverage your prior experience, knowledge of industry trends, and personal creativity to develop and drive new and innovative solutions that 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 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 offense always.
Embrace and embody Nike’s core values (maxims) in your work and interactions with peers, stakeholders, and direct reports. Model clarity and accountability as a leader of Nike. Communicate effectively, build trust and strong relationships across the company, do the right thing.
Be a mentor to teams, individuals, and technical leaders to foster motivated contributors, collaborative stakeholders, and high-performing teams. Set high standards for engineering excellence, MLOPs, operational excellence, and drive innovation.
Requirements & Skills:
A proven technical leader with deep knowledge and experience in delivering production-grade software at scale. Able to dive deep into code and architecture to provide an informed opinion on the benefits/trade-offs of different design choices.
Comparable Bachelor’s Degree in computer science, software engineering, or applicable field (degree or Ph.D. preferred).
10+ years of experience across multiple roles in developing productionized code in software or data engineering, machine learning, or a related field (experience delivering analytics and ML products preferred).
4+ years in a leadership or management role using Agile frameworks, processes, and team structures.
Familiarity with ETL, ML, or analytics technologies such as Scikit-learn, Dask, Tensorflow, Kubeflow, Spark, EMR, or similar platforms and frameworks.
Must be able to meaningfully communicate, written, orally, and visually, technical topics with stakeholders and articulate the benefits and tradeoffs of each solution and connect them to business value.
Fluency in the application of open-source technologies and the impact of standardized platforms. Ability to make build/buy decisions in the area of Data Science, AI, & ML.
Must be able to articulate and evangelize MLOps, code management workflows, infrastructure as code, and the lifecycle of model development from experimentation to production and measurement. This will include a deep knowledge of software architecture and engineering best practices, especially modern cloud computing stacks for deploying machine learning at scale.
Strong skills in team leadership and building positive relationships across Product, Architecture, and Engineering, and ability to influence decisions and changes across loosely coupled teams.
