Senior Data Scientist, Simulation Capacity Optimization
Waymo · Mountain View, United States · 26d ago
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.
Our Simulation team is at the heart of this mission, enabling us to safely and rapidly iterate on the Waymo Driver. We run billions of miles of simulations, creating a massive and complex demand for technical infrastructure resources (CPU, GPU, TPU, Storage).
We are establishing a new team called CORPIO (SimEval Capacity Operations, Resource Planning, Infrastructure Optimization). This team is tasked with building a critical capability for Waymo: data-driven, strategic capacity planning and resource optimization. We are looking for a Senior Data Scientist to bridge the gap between sophisticated mathematical modeling and production-scale infrastructure automation. You will be responsible for building the technical systems that forecast demand, optimize resource allocation, and automate infrastructure management, ensuring our simulation environment is both high-performance and cost-effective.
As a Senior Data Scientist on the CORPIO team, you will:
- Infrastructure Modeling & Automation: Design and build production-grade systems and pipelines to automate capacity planning, demand management, and quota allocation.
- Quantitative Forecasting: Implement and maintain sophisticated models for infrastructure demand forecasting, incorporating architectural shifts, peak loads, and time-shifting opportunities.
- Resource Optimization Algorithms: Develop and deploy algorithms to optimize resource utilization across a heterogeneous fleet (CPU, GPU, TPU) and diverse supply models (on-demand vs. reserved).
- Data Pipeline Engineering: Architect and maintain robust data pipelines that ingest infrastructure telemetry and demand driver signals to feed forecasting and optimization engines.
- Outcome Analysis: Build systems to translate resource plans into tangible outcomes (e.g., queue lengths, user demand fulfillment) and develop attribution models for capacity imbalances.
- Cross-Functional Collaboration: Partner with Simulation, Infrastructure, and Finance teams to translate business requirements into technical specifications and automated solutions.
- Technical Leadership: Provide technical guidance on the intersection of quantitative modeling and systems engineering, mentoring junior members and influencing the technical roadmap for CORPIO.
You have:
- Bachelor's degree in a quantitative field (e.g. Statistics, Mathematics, Physics) or equivalent practical experience.
- 5+ years of industry experience solving data science problems, or a PhD in a quantitative field and 3+ years of industry experience
- Expertise using advanced statistical methods in an applied setting; familiarity with ML systems/models
- Strong background in quantitative methods, such as optimization, statistical modeling, or time-series analysis.
- Demonstrated knowledge of Python/SQL/R data analysis libraries and packages
We prefer:
- PhD or Master's degree in a quantitative field
- Experience in Capacity Engineering or Infrastructure Optimization at scale.
- Familiarity with ML-driven forecasting and optimization techniques.
- Experience with financial modeling or cost-benefit analysis of technical infrastructure.
- Experience building automation tools for resource management and quota allocation.
- Knowledge of simulation workloads or high-performance computing (HPC) environments.
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.