About the position
As a Machine Learning Engineer within the AI/ML team at NIKE, Inc., you will play a crucial role in developing advanced analytics systems that have a direct impact on the business. This position involves working on a cross-disciplinary team that includes data, API, infrastructure, information security, and machine learning professionals. Your primary focus will be on enabling data-driven decision-making across various organizations within Nike. You will be at the intersection of machine learning and software engineering, also known as MLOps, where you will create high-quality solutions that power Nike's operations. In this role, you will collaborate with a team that thrives on building innovative solutions from the ground up, utilizing the latest technologies in statistical, unsupervised, supervised, and machine learning models at a global scale. The work environment is collaborative and academic, promoting skill development, mentorship, and the sharing of knowledge within the analytics and engineering communities. This culture is fostered by intellectual curiosity, fun, openness, and diversity, making it an exciting place to work. You will be an integral member of cross-functional engineering teams that deliver solutions to unlock machine learning capabilities for Nike. Your responsibilities will include analyzing and profiling data to uncover insights, cleaning and preparing data for analysis and model creation, and tracking model accuracy and performance. You will also apply various machine learning methods to datasets and assist in building APIs and software libraries that support the adoption of models in production. Staying current with industry trends and recommending relevant technologies in analytics, machine learning, and data science will be essential to your success in this role. Embracing Nike's core values and effectively communicating with peers and stakeholders will be key components of your daily activities. You will be expected to build trust and strong relationships across the company while doing the right thing in all your interactions.
Responsibilities
- Develop advanced analytics systems that impact business decisions.
- Work on a cross-disciplinary team to enable data-driven decision making.
- Create high-quality solutions at the intersection of machine learning and software engineering (MLOps).
- Analyze and profile data to uncover insights for scalable solutions.
- Clean, prepare, and verify the integrity of data for analysis and model creation.
- Track model accuracy, performance, relevance, and reliability.
- Apply machine learning and collaborative filtering methods to datasets.
- Build APIs and software libraries to support model adoption in production.
- Stay current with industry trends and recommend relevant technologies in analytics and machine learning.
- Communicate effectively and build trust with peers and stakeholders.
Requirements
- Bachelor Degree or a combination of relevant education, training, and experience.
- Understanding of Machine Learning applications and the lifecycle of an ML application in production.
- Ability to articulate the role of MLOps in model development from experimentation to production.
- Strong communication skills to convey technical topics effectively.
- Experience working in or collaborating with distributed teams.
- Understanding of data structures, algorithms, and data solutions.
- Experience in applying Python (or another ML language) and SQL to ML and data engineering tasks.
- Familiarity with ETL, ML, or analytics technologies such as Scikit-learn, Pandas, OpenCV, NumPy, TensorFlow, or similar platforms.
- Awareness of data science platforms (like Databricks or SageMaker) and distributed engines (like Spark and AWS cloud).
- Fluency in open-source technologies and standardized platforms in Data Science, AI, & ML.
Nice-to-haves
- Interest in the potential of Generative AI to accelerate development and data science tasks.
- Experience with the deployment of Generative AI solutions in the enterprise.
Benefits
- Generous total rewards package
- Casual work environment
- Diverse and inclusive culture
- Professional development opportunities
