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Promotions and incentives — from promo mechanics and budget planning through targeted, personalized incentives as the rider base grows.
Fleet supply — how many vehicles are in service each hour and where they stage and charge, so fleet and pricing decisions optimize against the same objectives.
This is a hands-on role: you will write the PRDs that define what each system optimizes for, specify the models that make those decisions, and prove they worked. You will sit in the Commercial organization and partner closely with Data Science, Fleet Operations, Finance, and the product and engineering teams that build these systems. It is ideal for a marketplace product manager who has owned a pricing, incentives, matching, or supply lever and wants to shape how an autonomous mobility business makes money.
In this role you will:
Set the objectives that pricing, promotions, and fleet deployment optimize against, from the weekly supply plan to real-time dispatch, so that decisions made in separate systems add up to one commercial plan.
Partner day to day with Data Science on elasticity estimation, price effects modeling, demand and supply forecasting, supply planning, and marketplace simulation, bringing a clear point of view on what to build.
Set the priorities for the vertical: size the expected business impact and effort of each initiative with the science and engineering leads, stack rank them, and re-rank each quarter as results come in.
Drive delivery across the teams that build these systems, managing dependencies, holding the schedule, and escalating early when a date is at risk.
Design the experiments and simulation studies that gate launches, set ship criteria and guardrail metrics before launch, make the ship call, and write the readout against the sizing that justified the work.
Run the weekly operating cadence with Commercial Operations, Fleet Operations, and Finance: diagnose variance, propose changes to live levers, and present lever performance and roadmap status to leadership.
Qualifications
8+ years in product management, technical program management, or marketplace strategy roles, including significant time on marketplace products — ride-hail, delivery, travel, or similar — where pricing, incentives, matching, or supply planning was the product.
Demonstrated ability to specify an optimization problem in writing: given "we should price better," you can return an objective, its constraints, the data it needs, the guardrails it must respect, and the experiment that validates it.
A track record of driving a roadmap to completion through teams that do not report to you, changing priorities with evidence rather than authority.
A quantitative foundation: a degree in economics, operations research, statistics, or a similar field, and practical experience applying it — specifying or building models, running experiments, and reading their results.
Clear writing and presentation skills: PRDs that engineers and scientists can build from, and readouts that leadership can decide on.
The judgment to know which decisions need a model and which need a defensible heuristic shipped this week, in a business that is standing up its first markets.
Bonus Qualifications
Pricing or revenue management for a capacity-constrained business — airlines, hotels, freight, logistics, or energy — where the asset, not the labor pool, is the constraint.
Experience taking a pricing, incentives, or fleet management system from zero to production, including the operator tooling, data model, and rollback plan.
Deep technical grounding from graduate study in economics, operations research, statistics, or machine learning.
Hands-on experience with experiments where units interfere (geo and time splits, switchbacks, holdouts) and with simulation when a live test is not feasible.
Original formatting and links
As the first product lead for commercial systems at Zoox, you will own the vision and priorities for marketplace optimization in our ride-hail business — how we price, how we promote, and how we deploy a fixed fleet across the hours and zones of each market — and turn them into the requirements the teams building those systems plan against. Because we own and operate our vehicles, supply is fixed in the short run, and these levers are how we trade growth against profit week to week. They include:
Planned pricing — the rate card and its shaping by time of day and geography, per-zone fees, and the controls operators use to manage a market before any model intervenes.
Dynamic pricing — the real-time engine that balances a fixed fleet against live demand, weighing growth against profit market by market.
Promotions and incentives — from promo mechanics and budget planning through targeted, personalized incentives as the rider base grows.
Fleet supply — how many vehicles are in service each hour and where they stage and charge, so fleet and pricing decisions optimize against the same objectives.
This is a hands-on role: you will write the PRDs that define what each system optimizes for, specify the models that make those decisions, and prove they worked. You will sit in the Commercial organization and partner closely with Data Science, Fleet Operations, Finance, and the product and engineering teams that build these systems. It is ideal for a marketplace product manager who has owned a pricing, incentives, matching, or supply lever and wants to shape how an autonomous mobility business makes money.
In this role you will:
Set the objectives that pricing, promotions, and fleet deployment optimize against, from the weekly supply plan to real-time dispatch, so that decisions made in separate systems add up to one commercial plan.
Partner day to day with Data Science on elasticity estimation, price effects modeling, demand and supply forecasting, supply planning, and marketplace simulation, bringing a clear point of view on what to build.
Set the priorities for the vertical: size the expected business impact and effort of each initiative with the science and engineering leads, stack rank them, and re-rank each quarter as results come in.
Drive delivery across the teams that build these systems, managing dependencies, holding the schedule, and escalating early when a date is at risk.
Design the experiments and simulation studies that gate launches, set ship criteria and guardrail metrics before launch, make the ship call, and write the readout against the sizing that justified the work.
Run the weekly operating cadence with Commercial Operations, Fleet Operations, and Finance: diagnose variance, propose changes to live levers, and present lever performance and roadmap status to leadership.
Qualifications
8+ years in product management, technical program management, or marketplace strategy roles, including significant time on marketplace products — ride-hail, delivery, travel, or similar — where pricing, incentives, matching, or supply planning was the product.
Demonstrated ability to specify an optimization problem in writing: given "we should price better," you can return an objective, its constraints, the data it needs, the guardrails it must respect, and the experiment that validates it.
A track record of driving a roadmap to completion through teams that do not report to you, changing priorities with evidence rather than authority.
A quantitative foundation: a degree in economics, operations research, statistics, or a similar field, and practical experience applying it — specifying or building models, running experiments, and reading their results.
Clear writing and presentation skills: PRDs that engineers and scientists can build from, and readouts that leadership can decide on.
The judgment to know which decisions need a model and which need a defensible heuristic shipped this week, in a business that is standing up its first markets.
Bonus Qualifications
Pricing or revenue management for a capacity-constrained business — airlines, hotels, freight, logistics, or energy — where the asset, not the labor pool, is the constraint.
Experience taking a pricing, incentives, or fleet management system from zero to production, including the operator tooling, data model, and rollback plan.
Deep technical grounding from graduate study in economics, operations research, statistics, or machine learning.
Hands-on experience with experiments where units interfere (geo and time splits, switchbacks, holdouts) and with simulation when a live test is not feasible.
