About this role
Develop forecasting models for fleet availability, supply hours, staffing, operational workload, charging demand, and infrastructure needs Convert spreadsheet-based planning processes into reproducible, scalable, and well-documented analytical workflows Evaluate model performance through backtesting, forecast-versus-actual reporting, and clearly defined accuracy measures Build scenario models that help leaders understand the staffing, cost, and infrastructure implications of operational changes and market growth [placeholder] Combine statistical methods, machine learning, and AI-enabled tools to improve forecasts and make model outputs easier to interpret Prototype AI-powered decision-support tools that explain forecast drivers, identify emerging risks, and allow leaders to explore planning scenarios Partner with Workforce Management, Finance, Strategy, Data Science, and operational leaders to align assumptions and establish a shared operating forecast Currently pursuing a master’s degree in data science, statistics, computer science, operations research, industrial engineering, economics, applied mathematics, or another quantitative field Experience with forecasting, statistical modeling, optimization, simulation, or machine learning through coursework, research, internships, or professional projects Communicate model outputs An AI-native technical skill set, with demonstrated experience using LLMs, coding copilots, APIs, or agent frameworks to develop analytical products or automate technical workflows Strong understanding of model evaluation, uncertainty, explainability, and human oversight. Ability to select the right analytical approach for the problem rather than defaulting to the most complex model. Currently enrolled in a B.S. or M.S. program in a relevant discipline. Strong communication, problem-solving, and cross-functional collaboration skills Available to commit to a minimum three-month assignment. Able to commit a minimum of 20 hours per week. Able to work on-site in Foster City CA. Must adhere with Zoox confidentiality requirements, including refraining from using or sharing proprietary company information outside of Zoox, such as in academic research, theses, publications, or presentations.