Applied Scientist

At Waabi we are building the next generation of autonomous driving technology leveraging a revolutionary approach to Physical AI. Central to our success is the ability to monitor and report on the performance of our software platform and fleet with rigor and reliability.

You will...

- Collaborate with cross-functional teams to provide comprehensive analysis on autonomy system performance across Waabi World (Simulation) Track and Public Road testing.

- Develop and maintain sophisticated cross-modal dashboards that provide a unified and trustworthy view of the performance of our systems tracking progress toward critical company milestones.

- Partner with Data Platform and Evaluation teams to shape the evolution of eval tooling and pipelines ensuring our infrastructure remains scalable automated and capable of validating complex system requirements across all testing modalities.

- Act as a key partner to Safety and Autonomy teams ensuring that our automated reporting faithfully captures the technical intent and requirements of our system KPIs.

- Proactively monitor and investigate anomalies in metrics to ensure stakeholders are making decisions based on reliable high-fidelity data.

Qualifications

- Minimum of 3 years of professional experience in an analytical role (e.g. Data Science Systems Evaluation or Product Analytics).

- BS/MS in a quantitative field such as Computer Science Engineering Statistics Physics or similar.

- Strong command of SQL and familiarity with Python for data manipulation and prototyping.

- Experience building clear automated dashboards using modern BI tools or custom frameworks.

- Experience working with internal cross-functional partners/stakeholders

- Open-minded and collaborative team player with willingness to help others

- Passionate about self-driving technologies solving hard problems and creating innovative solutions.

Bonus/nice to have:

- Experience in the Autonomous Vehicle or Robotics industry.

- Experience with large scale databases and analytics

- Practical understanding of statistical concepts (e.g. hypothesis testing significance) as applied to real-world performance measurement