Lead Data Scientist
New
Based in the United StatesFull-TimeLead
SalaryCompetitive base salary with eligibility for annual performance-based bonus
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Job Details
- Experience
- 5–8 years
- Required Skills
- Machine LearningComputer Vision
Requirements
- Master’s or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
- 5–8 years of experience in applied machine learning, computer vision, multimodal systems, or perception-focused AI.
- Strong experience designing evaluation frameworks, metrics, and validation systems for AI or data-driven products.
- Ability to connect model behavior, data quality, and product outcomes in ambiguous or evolving technical environments.
- Experience translating research experiments into production-ready insights and engineering decisions.
- Strong analytical thinking with the ability to define structured evaluation approaches for complex systems.
- Excellent written and verbal communication skills, with the ability to clearly explain technical findings to diverse stakeholders.
Responsibilities
- Own and define the end-to-end evaluation and quality strategy for advanced AI, computer vision, and perception systems.
- Design and implement metrics to assess output quality, structural fidelity, temporal consistency, robustness, and downstream usability.
- Develop data validation strategies and quality frameworks to improve model performance and real-world applicability.
- Lead failure analysis, artifact auditing, and taxonomy development to identify system weaknesses and improvement opportunities.
- Establish release readiness criteria, human review protocols, and acceptance thresholds for complex AI systems.
- Translate evaluation results into data strategy decisions, experiment prioritization, and modeling improvement roadmaps.
- Partner with cross-functional teams to ensure model outputs align with product requirements and operational constraints.
- Build reporting and experiment frameworks that enable evidence-based decision-making across AI development cycles.
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