• Work with a team of product managers, data scientists, data engineers and architects to build targeted products that help customers solve and answer specific problems.
• Be hands on with code (Python, SQL) on cloud based ML technologies (Databricks, Snowflake) to explore data, design features, and build models.
• Design, build, train, and monitor predictive and anomaly detection models using primarily structured data to detect changes across Grainger’s millions of customers.
• Build, test, and deploy customer-facing analytical and ML endpoints, APIs, and trigger-based models that are a mixture of business logic, model, and data.
• Work as the first ML Scientist in a future small team to build models to support our sellers through the development of proactive models that identify changes in customer behavior and recommend best actions for sellers on the basis of those changes.
• Collaborate with researchers, software developers, business leaders, and end data users to define product requirements and provide analytical support.
• Articulate concepts and generate visual representations of model performance.

• Master's degree in a field such as Statistics, Applied Mathematics, Physics, Engineering, Computer Science, or Economics, or equivalent experience.
• Experience building, deploying, and maintaining production machine learning solutions with SQL and Python.
• Experience using cloud based development and analytics tools including Databricks, Snowflake, and MLFlow to create new and novel machine learning pipelines.
• Ability to set and define model and project based metrics and outcomes, build systems to deliver on those outcomes, and communicate those outcomes to the rest of the organization.
• Experience working directly with engineering teams to spec out data pipelines and work with them to deploy those pipelines.
• Relevant work experience in an analytical role involving data extraction, analysis, and communication of findings to project stakeholders (both technical and non-technical) and end users.

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