3 days ago
Base Salary
$134k - $224k/yr
Responsibilities
- Lead scalable, automated processes for large-scale data analysis, model training, validation, and deployment.
- Implement and continuously improve machine learning and operations research models in partnership with data scientists and cross-functional teams.
- Design and maintain ETL pipelines and automate system operations to support reliable data flow and model performance.
- Build internal tools and web applications integrated with machine learning and ERP systems.
- Evaluate and integrate emerging technologies aligned with strategic goals.
- Ensure the reliability and maintainability of deployed machine learning solutions using software engineering and DevOps practices.
- Mentor team members on technical execution, architecture decisions, and career development.
- Create architectural diagrams and documentation supporting system design, scalability, and knowledge sharing.
Requirements
- Bachelor's degree in computer science, engineering, or a related field is required.
- Master's degree in a related technical field or equivalent practical experience is preferred.
- At least 3 years of experience with production machine learning, optimization, modern data tools, and scalable engineering practices.
- Experience with AWS services including S3, ECR, and Secrets Manager.
- Production machine learning experience using Python.
- Experience with mathematical optimization and solvers such as Gurobi.
- Experience developing web applications integrated with machine learning and ERP systems.
- Knowledge of operations research and optimization modeling.
- Experience with databases such as Snowflake and DuckDB and with SQL.
- Experience with Kubernetes and Docker.
- Understanding of machine learning concepts including time series forecasting and clustering.
- Knowledge of version control and CI/CD tools such as GitHub and GitHub Actions.
- Experience automating workflows with Astronomer or Airflow.
- Experience building and supporting solutions in Databricks.
- Experience with machine learning frameworks and libraries such as PyTorch and scikit-learn.
Benefits
- Hybrid work location.
- Medical, dental, vision, and life insurance beginning on day one, plus six free licensed-therapist sessions annually.
- 18 paid time off days annually for full-time employees and six company holidays per year.
- 6% company contribution to a 401(k) retirement savings plan each pay period with no employee contribution required.
- Employee discounts, tuition reimbursement, student loan refinancing, and financial counseling, education, and tools.
- Maternity support programs, nursing benefits, up to 14 weeks of paid leave for birth parents, and up to four weeks for non-birth parents.
Tech Stack
Categories
Data EngineeringML Engineering
About Grainger
W.W. Grainger is a public B2B distributor of maintenance, repair, and operations (MRO) supplies, safety gear, and facility products for businesses. It sells through a high-touch distribution network and its Endless Assortment e-commerce brands Zoro and MonotaRO, alongside services like inventory management and technical support. Founded in 1927 and headquartered in Lake Forest, Illinois, Grainger serves more than 4.5 million customers and reported $17.2 billion in 2024 revenue.