Data Scientist, Fleet Orchestration Algorithms
Zoox
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- Pay
- $177K to $217K
- a year
- Location
- Foster City, CA
- Work style
- On-site
- Job type
- Full time
- Posted
- Oct 21
- 1 day ago
Career path
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About the company
Zoox
Automotive · 1,001 to 5,000 employees
What you'll do
- Piece together the constituent components into a coherent, easy-to-use simulation environment
- Fill in the gaps in the simulation environment
- Build generalizable mechanisms so data scientists can measure algorithm impact on fleet efficiency
What you need
- PhD or MS with 2+ years of relevant industry or research experience, in operations research, robotics, controls, transportation, computer science, applied math, or a related field Deep expertise in at least one of: combinatorial/integer optimization, stochastic optimization or control, reinforcement learning/approximate DP, network flows, or marketplace/demand modeling, plus the curiosity to work across the rest Strong coding skills in Python, C++, or Rust; you write clean, tested, version-controlled code Ability to turn ambiguous real-world problems into well-defined models and explain the tradeoffs to non-specialists Good communication and collaboration skills
- Internship or research experience in ride-hailing, delivery, logistics, or AV fleets (dispatch, routing, rebalancing, pricing) Algorithms deployed in production or used on real operational data Publications in operations research, transportation, or robotics venues
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Read the full posting here
The Core Data Science team builds the products and data assets that let teams across Zoox measure and improve the autonomy and operational performance of our fleet. In this role, you'll focus on fleet simulation — the environment our data teams use to develop, test, and evaluate the algorithms that orchestrate our autonomous fleet (dispatch, routing, and vehicle assignment) and to understand how those algorithms perform before they reach the road.
You’ll piece together the constituent components into a coherent, easy-to-use simulation environment, filling in the gaps, and building generalizable mechanisms so data scientists can measure their algorithm's impact on fleet efficiency. You'll join a diverse, experienced team with rapidly growing scope while working with one of the industry's most valuable autonomous vehicle datasets.