The Safety Evaluation team stands as the gatekeepers of safety, weaving expertise in data analysis and robust validation into the very fabric of our autonomous driving systems. We collaborate closely with engineers to ensure the evaluation of ML-heavy-systems rests on a foundation of safety principles. Our expertise seeps into every layer of our safety-critical systems, ensuring each decision made by our vehicles is rooted in rigorous analysis and sound judgment. Our work extends beyond assessment, venturing into the realm of innovation as we craft novel ML techniques uniquely tailored to the complexities of collision evaluation. Ultimately, our team paves the way for safer roads and a future where Waymo diver glides confidently through the complexities of the real world.

As a Staff Data Scientist specializing in Safety Evaluation at Waymo, you'll play a critical role in ensuring the safety and reliability of autonomous vehicles. Your responsibilities will span the entire spectrum of data analysis, model development, and evaluation. While each day at Waymo is different, your primary responsibilities will include: 

• Develop safety evaluation frameworks that are critical to ML model improvements.
• Design and implement robust data analysis pipelines and validation frameworks.
• Develop and apply statistical methods and ML models to analyze historical/simulation data and assess safety performance.
• Collaborate with cross-functional teams like Simulation and Planning to ensure comprehensive safety evaluation across the autonomous driving system.

At a minimum we’d like you to have:

• PhD in a quantitative field (IE Statistics, Mathematics, Physics)
• 7+ years of industry experience solving large-scale complex problems
• Proven expertise using advanced statistical methods (e.g., ML models, hypothesis testing, causal analysis)
• Ability to interpret and work with data in the presence of ambiguity (e.g., confounding factors, data sparsity or quality challenges)
• Demonstrated knowledge of Python/SQL/R data analysis libraries and packages
• Ability to take ambiguous and complex business requirements and propose a technical solution

It’s preferred if you also have:

• Experience solving problems related to Autonomous Driving
• Experience in adjacent relevant areas like Advanced Machine Learning (Deep Learning and Diffusion models), Traffic Modeling, Safety Evaluation or Prediction
• Hands-on experience with Google infra tools like PLX, Borg, GCL, etc

#li-hybrid