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External opportunity

Staff Product Data Scientist, Marketplace ML

Mountain View, CA, USA; San Francisco, CA, USA / Mountain View, California, United States / San Francisco, California, United States Not specified

Added 4 Nov 2026 · Engineering

Job description

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Waymo’s Product Data Science team works cross-functionally with Engineering, Product and Operations to help the company make the most informed decisions using data. Our team collaborates on high-impact projects across the company — from driving quality and operational efficiency to market analysis and rider satisfaction scores — we help to safely and efficiently scale the Waymo Driver. We are data-driven, curious, open-minded, and adapt quickly to new information. This role follows a hybrid work schedule and you will report to a Product Data Science Lead You will: Partner with Engineering, Product, and Data Science to build ML models that improve the efficiency of Waymo's marketplace Build models that predict conversion, wait times, and retention, and estimate the value of a vehicle in different positions Apply forecasting models for demand and supply time series Develop novel reinforcement learning models that continuously tune pricing, matching, and positioning parameters Design, run, and evaluate experiments for new models Present findings and recommendations to Waymo leadership on a regular basis You have: 8+ years of industry experience applying ML to production problems Strong foundation in algorithms Proficiency in Python and SQL Hands-on experience building and deploying ML and neural network models Experience applying reinforcement learning to real-world systems We prefer: Experience building neural networks in TensorFlow, or JAX Experience at a ride-hailing or two-sided marketplace company MS or PhD in Computer Science, Statistics, Operations Research, or a related field The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $230,000—$284,000 USD
Pay, skills & work eligibility

Compensation

$230,000–284,000

Pay basis not specified · Pay period not stated

Identified in listing text. No currency conversion or estimated salary.

View compensation wording
Salary Range $230,000—$284,000 USD

Location, eligibility & working hours

Listed work location
Mountain View, CA, USA; San Francisco, CA, USA / Mountain View, California, United States / San Francisco, California, United States
Visa sponsorship
Not stated clearly
Relocation support
Not stated clearly
Working hours / time zone
Not stated clearly

Remote does not automatically mean work from any country. A location match does not confirm work authorization, sponsorship or relocation eligibility.

Requirements at a glance

Experience mentioned: 8+ years

PythonSQL

Named skills identified in the listing. A mention is not always a requirement; read the full description and employer conditions.

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