About this Data Scientist, Fleet Orchestration Algorithms role at Zoox
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 develop the algorithms that run our robotaxi fleet: matching vehicles to riders and other missions, staging vehicles where and when they're needed, routing, and pricing.
You'll also model what goes into those decisions: rider demand, the value of the data our R&D fleet collects, servicing needs, and business inputs like events and policies. You'll be the optimization generalist on the team, working alongside specialists in marketplace economics, forecasting, and autonomy, and you'll see your algorithms run on a real autonomous fleet. You’ll join a diverse, experienced team with rapidly growing scope while working with one of the industry’s most valuable autonomous vehicle datasets.
In this role, you will:
- Design, prototype, and help deploy algorithms for vehicle assignment, staging, routing, and pricing
- Model the inputs to orchestration (rider demand, data-collection value, servicing needs, events, and policies) as objectives and constraints
- Work with specialists in marketplace economics, forecasting, and autonomy to turn their models into decisions the fleet acts on
- Measure impact on revenue, utilization, and rider experience through simulation, offline evaluation, and online experiments
- Contribute to how we evaluate and roll out orchestration improvements
Qualifications
- 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
Bonus Qualifications
- 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
