Who You'll Work With
You will work within the Supply Chain Planning & Technology (SCPT) organization, partnering with Product Managers, Data Scientists, Engineering teams, and Supply Chain stakeholders across Deployment Optimization (DO), Controlled Allocation (CA), and Dynamic Marketplace Allocation (DMA). This role drives advanced analytics and AI-led decisioning across supply chain platforms.
Who We Are Looking For
We are looking for a
Lead Machine Learning Engineer who can bridge data science and production-grade engineering to solve complex supply chain problems at scale. You bring strong system design skills, hands-on ML expertise, and the ability to lead engineering teams in delivering enterprise-grade AI solutions.
You are comfortable working in ambiguous environments, making architectural decisions, and influencing technical direction across teams. You have deep experience in building scalable ML systems, operationalizing models, and ensuring performance, reliability, and governance in production environments.
- 8–10 years of experience in software engineering and machine learning, with 2+ years in a technical leadership role
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or related field (or equivalent combination of education and experience)
- Strong programming expertise in Python or R
- Hands-on experience with ML frameworks (PyTorch, TensorFlow, Keras) and MLOps practices
- Strong experience with cloud platforms (AWS, Azure, Google Cloud Platform) and containerization (Docker, Kubernetes)
- Solid data engineering experience with tools and platforms such as Databricks, Apache Spark, Hive, and Airflow is good have
What You'll Work On
You will design and deliver scalable machine learning solutions that power supply chain decision-making across Nike. You will lead the end-to-end lifecycle of ML systems, from data ingestion and model development to deployment and real-time monitoring.
- Architect and build scalable ML systems leveraging optimisation, NLP (Natural Language Processing), and advanced analytics
- Lead end-to-end ML lifecycle (MLOps) including data pipelines, model training, deployment, and monitoring
- Provide technical leadership and mentorship to engineering and data science teams
- Build and maintain production-grade ML pipelines using CI/CD practices
- Optimize model performance, latency, and scalability while ensuring data security and governance
- Collaborate with product and business stakeholders to translate complex problems into ML-driven solutions
- Evaluate emerging technologies (Generative AI, LLMs, agent-based workflows) and drive adoption where relevant
