Waabi is hiring a Applied Scientist in Toronto!
Waabi, an autonomous transportation company founded by Raquel Urtasun, is hiring an Applied Scientist in Toronto to support rigorous performance evaluation of its self-driving software, platform, and fleet. The role sits between Evaluation, Systems & Safety, and Autonomy teams, with a focus on creating a unified, trustworthy view of system performance across simulation, track, and public-road testing. This is a hands-on, highly cross-functional position for someone who enjoys turning complex, multi-modal data into clear dashboards, reliable metrics, and actionable insights that guide company milestones.
Key responsibilities
- Analyze autonomy-system performance across simulation, track, and public-road testing in collaboration with cross-functional teams.
- Build and maintain cross-modal dashboards that give stakeholders a consistent view of system performance and progress toward milestones.
- Partner with Data Platform and Evaluation teams to improve scalable, automated evaluation tooling and pipelines.
- Work closely with Safety and Autonomy teams so automated reporting accurately reflects technical intent and KPI requirements.
- Proactively monitor metrics, investigate anomalies, and help ensure decisions rely on high-fidelity data.
Key requirements
- 3+ years of professional experience in an analytical role such as data science, systems evaluation, or product analytics.
- BS/MS in computer science, engineering, statistics, physics, or another quantitative field.
- Strong SQL skills and familiarity with Python for data manipulation and prototyping.
- Experience building clear, automated dashboards using modern BI tools or custom frameworks.
- Demonstrated ability to work with internal cross-functional partners and stakeholders.
- Collaborative, open-minded approach and enthusiasm for self-driving technology and hard problems.
- Seniority: Mid-level (3+ years experience)
- Work mode: Not specified
- Key skills: SQL, Python, dashboards, BI tools, data analysis, cross-functional collaboration, anomaly investigation, statistics, autonomous vehicles, robotics
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