Intern - Operations Data Science
$25.00-$30.00/hour
Build models, optimizers and AI tools that change how a nationwide moving and storage operation runs, and leave behind work the team keeps using.
PODS moves and stores for customers across the country. Our Operations Data Science & AI team builds what helps the business see and run itself better: capacity forecasts, routing and planning optimizations, research on hard operational questions, and AI tools that automate analysis and decisions once done by hand.
You’ll report to the Director of Operations Data Science & AI and work alongside senior data scientists on one of two tracks:
Data Science & Research: statistical modeling, optimization and machine learning
AI Tool Building: AI-powered tools and agents that automate operational workflows
On either track you’ll own one or two focus projects with real deliverables, help steward the optimizers and tools already in production, and present your results to the team and operational stakeholders.
What you’ll do
Own a focus project. Build and validate a predictive model (demand or capacity forecasting, classification, anomaly detection) against actuals; formulate and solve a routing, scheduling or capacity optimization and quantify the gain; build an AI agent that automates a manual workflow; or frame a loosely defined operational question and deliver a recommendation.
Do real data science. Prepare and validate operational data, engineer features, and run model and optimization experiments with senior data scientists.
Build tools and automation. Create AI-powered tools, reproducible pipelines and automated reporting that replace manual work.
Steward what’s in production. Help maintain and monitor the team’s optimizers, models and tools, and bring improvement ideas with the analysis to back them.
Make results usable. Work with IT to integrate solutions into the platforms operational users rely on, maintain data models in Snowflake, and build dashboards and decision-support tools.
Work AI-first. Use Claude, Cursor, Copilot or similar to move faster on analysis, code and documentation, and verify every output before it ships.
What you’ll walk away with
A model, optimizer or AI tool in production that the team keeps using after you leave
Hands-on experience with Python, SQL, Snowflake, and modern ML, optimization and LLM tooling on live operational data
A clear understanding of how data science and AI improve a real physical operation
Mentorship from the Director and senior data scientists, plus exposure to IT and Operations partners
A presentation to stakeholders you can point to in your next interview
Who we’re looking for
Both tracks
Working knowledge of SQL and Python (pandas, scikit-learn, statsmodels or similar) from coursework, projects or a prior internship
Hands-on use of AI tools (Claude, Cursor, Copilot or similar) for analysis or coding, with the judgment to check the output
Able to communicate model outputs through clear visualizations and decision-support tools (Tableau, Power BI or similar)
Comfortable turning a loosely defined operational problem into a clear analytical question and a practical solution
Takes ownership of a deliverable from first draft to finished product, asks for help early, and explains results in plain language to both technical peers and operational leaders
Data Science & Research track
Coursework or project experience building and validating statistical or ML models (forecasting, regression, classification or anomaly detection)
Exposure to optimization methods such as linear or mixed-integer programming; a solver or library (Gurobi, Pyomo, OR-Tools, PuLP) is a plus
AI Tool Building track
Hands-on experience building AI-powered tools, agents or automations with LLMs and their APIs; prompt design, retrieval or agent frameworks are a plus
Nice to have: Snowflake or another cloud data warehouse; Airflow, APIs, containers or PySpark.
Job Qualifications
Currently in the final years of a bachelor’s…