Sr Staff Data Scientist, Simulation Capacity Optimization

<div class="content-intro"><p>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.</p></div><p>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.</p> <p>What you’ll do (Responsibilities):</p> <p>As the founding Lead Data Scientist of the SCORPIO team, you will:</p> <ul> <li>Define the vision, strategy, and technical roadmap for data-driven capacity planning and resource optimization within Waymo's simulation environment.</li> <li>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.</li> <li>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.</li> <li>Develop and propose actionable strategies for resource optimization, cost management, and risk mitigation to senior leadership, finance, and engineering teams.</li> <li>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.</li> <li>Spearhead the design and development of automated systems for demand management, quota allocation, and resource reassignment to enhance efficiency and responsiveness.</li> <li>Provide data-driven insights to influence the design of simulation products and user guidelines, promoting more efficient resource consumption patterns.</li> <li>Build and mentor a high-performing team, potentially including data scientists, business analysts, and software engineers.</li> </ul> <p> </p> <p>What we’re looking for (Minimum Qualifications):</p> <ul> <li>PhD or Master's degree in Data Science, Statistics, Operations Research, Computer Science, Industrial Engineering, or a related quantitative field.</li> <li>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.</li> <li>5+ years of experience in a technical leadership role, with a proven track record of defining strategy, setting technical direction, and leading complex projects.</li> <li>Strong expertise in statistical modeling, time series analysis, and forecasting techniques (e.g., ARIMA, Exponential Smoothing, regression models).</li> <li>Demonstrated ability to work with large-scale, complex datasets and experience with distributed computing environments.</li> <li>Proficiency in Python or R, including common data science libraries (e.g., pandas, NumPy, SciPy, scikit-learn).</li> <li>Expertise in SQL and experience with data warehousing solutions (e.g., BigQuery, etc.).</li> <li>Exceptional communication and collaboration skills, with the ability to con ... (truncated, view full listing at source)