At UVeye, we are transforming vehicle safety and reliability on a global scale. Since 2016, we’ve pioneered the world’s first fully automated vehicle inspection systems- powered by AI, machine learning, and computer vision.

With nearly $400 million in funding and partnerships with Amazon, GM, Volvo, and CarMax, UVeye is reshaping the automotive industry through intelligent inspection. Our team of 200+ innovators is united by a mission to build a smarter, safer future for mobility.

We are looking for a seasoned and impact-driven Data Scientist to join our team. In this role, you will extract actionable insights from product usage, customer behavior, and system telemetry, and work closely with R&D teams to analyze the behavior and performance of our web platform, and AI models in the field.

A Day in the Life & How You’ll Make an Impact:

• You will act as a bridge between product, customer, and technical teams, using data to illuminate how the system is being used, how the AI is performing, and where improvement opportunities exist.
• Lead product analytics, identifying bottlenecks in the inspection flow, feature adoption trends, and operational KPIs across customer sites.
• Perform deep dives into customer behavior, combining structured product usage data with customer support signals, NPS, and segmentation to drive retention and satisfaction.
• Analyze AI model outputs and edge-device telemetry to detect performance issues, drift, false positives/negatives, and rare failure patterns in the field.
• Collaborate with AI/ML engineers to define measurable benchmarks for model accuracy, inference time, and edge reliability.
• Monitor system-level telemetry for anomalies and degradations that impact customer experience- such as slow scan durations, poor image quality, or inconsistent classification outcomes.
• Build and own dashboards and alerting systems to track inspection quality, model health, and customer-facing impact.
• Design and evaluate A/B tests and experiments to measure product and algorithmic changes on real-world usage.
• Contribute to root cause analyses when errors or escalations occur, bringing a data-driven perspective to support resolution and product learning loops.