Lead Data Scientist

What's the role? We are hiring a Lead Data Scientist to own applied AI quality, data strategy, and downstream usefulness across advanced AI, computer vision, and perception systems. This person will define how we determine whether a system is working, where it is failing, what quality bar is required for release, and how data, evaluation, and applied modeling should evolve to improve the product. This is a senior role for someone who can bring rigor to ambiguous technical programs, establish evaluation systems, and translate model behavior into product decisions, data strategy, and concrete improvement loops. What You Will Own- Own the evaluation framework and quality strategy for advanced AI and vision systems- Define pass/fail metrics for output quality, structural fidelity, temporal consistency, label quality, robustness, and operational repeatability- Own data and validation strategy for improving model quality and downstream usefulness- Lead artifact auditing, failure taxonomy development, release-quality reporting, and evidence-based prioritization- Measure whether outputs are suitable for perception, mapping, generative AI, and customer-facing use cases- Partner with model, simulation, and platform owners to drive quality improvements and production-readiness decisions What You Will Do- Build and evolve metric suites for output quality, fidelity, repeatability, and downstream usefulness- Define human-review protocols and product acceptance thresholds for complex AI systems- Evaluate whether outputs preserve the structure, semantics, and consistency expected by downstream applications- Translate evaluation findings into data strategy, experiment priorities, and applied modeling opportunities- Help define dataset design, validation slices, and quality-improvement loops across the product- Create experiment and release reports that turn technical output into clear product decisions- Help prioritize what the team should fix next based on evidence rather than intu