Data Scientist III- Operations

Job Summary

As a Data Scientist on the Operations Data Science & AI team, you will report to the Director, Operations Data Science & AI and work with senior data scientists and operational stakeholders to develop optimization models, predictive models, and automated workflows. Your work will help PODS make better decisions across capacity planning, routing, scheduling, resource allocation, and other field operations.

• Develop optimization solutions:
• Build and support optimization models for capacity planning, routing, scheduling, and resource allocation.
• Formulate business problems using decision variables, objectives, and operational constraints.
• Assist in root-cause analysis to surface optimization and automation opportunities across field operations.
• Build, test, and maintain forecasting, regression, classification, and anomaly-detection models for operational problems.
• Prepare and validate data, engineer features, and evaluate model results.
• Build and automate workflows:
• Build reproducible data pipelines and automate recurring analyses, model runs, and reporting, replacing manual processes.
• Contribute to shared tooling, frameworks, and standards so that solutions are repeatable.
• Develop analytical assets and data models:
• Maintain data models in Snowflake that other analysts and downstream tools rely on.
• Create dashboards and decision-support tools that make results actionable.
• Document and communicate clearly:
• Document logic, methodology, and assumptions alongside every model, tool, or pipeline you build.
• Present findings and their limitations in plain language to the team and operational stakeholders.

As a Data Scientist on the Operations Data Science & AI team, you will report to the Director, Operations Data Science & AI and work with senior data scientists and operational stakeholders to develop optimization models, predictive models, and automated workflows. Your work will help PODS make better decisions across capacity planning, routing, scheduling, resource allocation, and other field operations.

• Develop optimization solutions:
  • Build and support optimization models for capacity planning, routing, scheduling, and resource allocation.
  • Formulate business problems using decision variables, objectives, and operational constraints.
  • Assist in root-cause analysis to surface optimization and automation opportunities across field operations.
• Develop predictive models:
  • Build, test, and maintain forecasting, regression, classification, and anomaly-detection models for operational problems.
  • Prepare and validate data, engineer features, and evaluate model results.
• Build and automate workflows:
  • Build reproducible data pipelines and automate recurring analyses, model runs, and reporting, replacing manual processes.
  • Contribute to shared tooling, frameworks, and standards so that solutions are repeatable.
• Develop analytical assets and data models:
  • Maintain data models in Snowflake that other analysts and downstream tools rely on.
  • Create dashboards and decision-support tools that make results actionable.
• Document and communicate clearly:
  • Document logic, methodology, and assumptions alongside every model, tool, or pipeline you build.
  • Present findings and their limitations in plain language to the team and operational stakeholders.

• Mathematical optimization:
  Hands‑on experience formulating and solving mixed‑integer linear programming models, including defining decision variables, objectives, and constraints.
• Optimization tools:
  Previous experience with Gurobi, Pyomo, OR-Tools, PuLP, CPLEX, or a similar optimization library or solver is required.
• SQL and Python fluency:
  Strong SQL on a modern cloud data warehouse, preferably Snowflake, and Python for analysis and model development.
• Applied machine learning:
  Experience building, testing, and validating forecasting, regression, classification, or other predictive models, with judgment about which method fits the problem.
• Workflow automation:
  Experience building reproducible data pipelines and automating recurring analyses and model workflows.
• Data visualization:
  Ability to…