Sr Staff Data Scientist, Simulation Capacity Optimization

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.

We are establishing a new team called SCORPIO (SimEval Capacity Operations, Resource Planning, Infrastructure Optimization). This team will be at the forefront of ensuring the efficient and effective use of Waymo's large-scale simulation compute, storage, and network resources. SCORPIO will develop the data-driven models, metrics, and processes to forecast demand, plan capacity, and optimize resource allocation, ultimately improving developer experience and maximizing return on infrastructure investments.

What you’ll do (Responsibilities):

As the founding Lead Data Scientist of the SCORPIO team, you will:

• Define the vision, strategy, and technical roadmap for data-driven capacity planning and resource optimization within Waymo's simulation environment.
• Lead the development and implementation of sophisticated forecasting models to predict demand for heterogeneous TI resources (CPU, GPU, Storage, Bandwidth, RAM) across various time horizons and simulation workflows.
• Design, build, and maintain robust capacity models, key metrics, and insightful dashboards to monitor resource utilization, identify current and future bottlenecks, and inform investment decisions.
• Develop and propose actionable strategies for resource optimization, cost management, and risk mitigation to senior leadership, finance, and engineering teams.
• Collaborate deeply with Simulation, Infrastructure, Finance, Product Management, and Engineering teams to understand demand drivers, usage patterns, system changes, and their impacts on resource needs.
• Spearhead the design and development of automated systems for demand management, quota allocation, and resource reassignment to enhance efficiency and responsiveness.
• Provide data-driven insights to influence the design of simulation products and user guidelines, promoting more efficient resource consumption patterns.
• Build and mentor a high-performing team, potentially including data scientists, business analysts, and software engineers.

What we’re looking for (Minimum Qualifications):

• PhD or Master's degree in Data Science, Statistics, Operations Research, Computer Science, Industrial Engineering, or a related quantitative field.
• 10+ years of experience in data science or quantitative analysis, with a significant focus on capacity planning, resource optimization, demand forecasting, or a closely related area.
• 5+ years of experience in a technical leadership role, with a proven track record of defining strategy, setting technical direction, and leading complex projects.
• Strong expertise in statistical modeling, time series analysis, and forecasting techniques (e.g., ARIMA, Exponential Smoothing, regression models).
• Demonstrated ability to work with large-scale, complex datasets and experience with distributed computing environments.
• Proficiency in Python or R, including common data science libraries (e.g., pandas, NumPy, SciPy, scikit-learn).
• Expertise in SQL and experience with data warehousing solutions (e.g., BigQuery, etc.).
• Exceptional communication and collaboration skills, with the ability to convey complex quantitative findings and recommendations clearly to diverse audiences, including executive leadership.

What will make you stand out (Preferred Qualifications):

• Direct experience in CapEx Engineering, Cloud Services Capacity Planning (e.g., AWS, GCP, Azure), or managing resources for large-scale compute/HPC infrastructure.
• Familiarity with simulation workloads, performance analysis, and distributed systems.
• Experience with financial modeling, cost-benefit analysis, and ROI calculations related to technical infrastructure.
• Experience building and deploying data pipelines and automation tools in a production environment.
• Experience hiring, growing, and nurturing a technical team.

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 lev