Grainger is looking to leverage data, advanced analytics, and machine learning to deliver tangible, long-term business value and measurable financial impact by designing, developing, and deploying scalable ML solutions that directly influence strategic business decisions and product capabilities.
Requirements
- 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
- Experience with databases (Teradata, Snowflake, S3) and data processing at scale
- Proven ability to apply deep learning and transformer-based modeling methods in production environments
- Familiarity with modern ML architectures, including embedding models, multimodal systems, or generative AI (LLMs, diffusion models) where applicable
Responsibilities
- Partner with business teams to understand problems, identify opportunities, and translate them into impactful ML solutions.
- Manipulate high-volume, high-dimensionality data from multiple sources, visualize patterns, anomalies, relationships, and trends, and perform feature engineering and selection
- Design, build, and deploy scalable ML models and pipelines from ideation to production, following best practices in MLOps and software engineering.
- Use advanced ML methods — including deep learning, NLP, LLMs, and time series forecasting — and selectively apply optimization approaches like linear programming or simulation to build scalable, data-driven solutions.
- Develop interactive analytical tools and applications (e.g., React, Streamlit) to visualize model outputs, simulate scenarios, and make insights actionable.
- Design and deploy scalable, automated ML workflows for data analysis, model development, validation, and deployment — integrating analytical products and APIs into business systems.
- Collaborate with business partners, engineering, MLOps/DevOps and Product teams to design and implement AI solutions that integrate predictive and prescriptive components
Other
- MS degree or PhD in a technical field such as Mathematics, Data Science, Applied Analytics, Operations Research, Computer Science, Applied Science or Engineering
- 3+ years of experience delivering end-to-end ML solutions at scale — from data ingestion through model deployment and monitoring
- Strong analytical and problem-solving mindset; able to translate complex business challenges into structured, data-driven solutions
- Excellent communication skills, with the ability to convey technical concepts to both technical and business audiences
- Ability to work in a remote environment
