RecentlyPostedJobs

Product Data Scientist - Marketplace Logistics

Waymo · On-site · Full-time · Mountain View, CA, USA, San Francisco, CA, USA · United States

Posted (30m ago)

Apply on Waymo’s site

Employer posted
First seen here
Repost check
Same job ID since we first saw it on Oct 20, 2026. If Waymo reposts it, we keep the original posting date.
Pay
$159k–$196k a year (from the description)
Level
Mid-level
Work setup
On-site
Tech mentioned
Python, SQL
Waymo hiring
51 roles posted in the last 30 days

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. In this hybrid role, you will report to a Staff Product Data Scientist. You will: Build novel optimization models to strategically assign and dispatch vehicles to high-value locations when they are off-hail Build machine learning models to improve vehicle value predictions and fleet utilization metrics Conduct offline and online experiments to monitor, analyze, and optimize model deployments in real-world environments Partner with engineering to translate optimization algorithms and predictive logic into automated, production-grade pipelines Translate complex statistical and optimization results into actionable insights that drive marketplace performance and product strategy You have: An advanced degree in a quantitative field (e.g., Operations Research, Industrial Engineering, Statistics, Computer Science) or equivalent practical experience At least 1 year of industry experience applying quantitative methods in a business environment Strong coding skills in Python and SQL, with a track record of building and manipulating data pipelines Practical experience building and deploying machine learning models Strong hands-on experience with optimization modeling and implementation using solver packages (e.g., CP-SAT, CPLEX, Gurobi) We prefer: Experience working at another ride-hailing or marketplace company Hands-on experience with experiment design and online evaluation (e.g., A/B testing, marketplace experimentation) Deep statistical knowledge and practical application of statistical theory 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 $159,000 — $196,000 USD

Apply on Waymo’s site

Waymo job ID: 5572

More Waymo jobs

All open Waymo jobs

We are not Waymo. The hiring company owns this listing. Reposts of the same requisition id are not shown as new.

All new jobs · Companies we watch · How dates work · Report an error