Grainger is looking to lead a team of machine learning scientists and engineers to design, develop, and deliver scalable ML and AI solutions that directly shape business decisions, improve operational effectiveness, and unlock new value across the company.
Requirements
- 5+ year’s of hands-on experience delivering production-grade machine learning solutions at scale
- Advanced proficiency in Python and SQL for data manipulation and model development
- Hands-on experience with machine learning frameworks and deployment tools (e.g., scikit-learn, PyTorch, TensorFlow, MLflow, REST APIs)
- Familiarity with containerization, CI/CD, and version control (Kubernetes, Docker, Git)
- Experience building interactive, model-driven applications using React, Streamlit, or similar frameworks
- Proven ability to apply deep learning and transformer-based modeling methods in production environments
- Solid understanding of MLOps practices, model registry, drift monitoring, and hyperparameter optimization
Responsibilities
- Drive the design, development, and delivery of scalable machine learning and deep learning models that improve the effectiveness and efficiency of core business operations
- Oversee the creation of robust ML pipelines — from ideation and prototyping to automated, production-grade systems
- Apply advanced methods such as classification, regression, NLP, deep learning, LLMs, time series forecasting, and Bayesian inference to build impactful solutions
- Encourage and support the development of interactive analytical tools (e.g. React, Streamlit) to visualize model outputs and enhance collaboration with business users
- Explore and apply optimization, simulation, and decision-science techniques to augment predictive models with prescriptive intelligence
- Implement rigorous model validation, monitoring, and continuous improvement practices (e.g., drift detection, retraining, hyperparameter tuning)
- Promote automation and standardization across ML workflows to improve scalability and reproducibility
Other
- Lead, mentor, and grow a team of machine learning scientists and engineers; set direction, define priorities, and foster technical excellence and collaboration
- Own the end-to-end relationship with business partners — understanding complex problems, identifying opportunities, and translating them into scalable ML solutions
- Stay current with emerging ML/AI technologies and research, evaluating their potential to drive innovation and competitive advantage
- Communicate analytical insights, model performance, and business impact clearly to executives and stakeholders
- Proven ability to lead cross-functional collaborations and influence technical and business stakeholders
