ML Data QA Lead

What you'll do

• Define quality strategies and roadmaps across the full data request lifecycle to identify failure modes and anomalies early.
• Translate ambiguous requirements into explicit, documented standards and decision rules for vendors and internal teams.
• Execute QA and QC checks during pilot and production phases to validate trends before delivery to R&D.
• Build and extend QA tools, review interfaces, and analysis pipelines using AI-assisted development.
• Integrate model-assisted checks into workflows by iterating on prompts and measuring agreement against human labels.
• Verify metrics, analyses, and artifacts independently before publishing them to project teams and partners.
• Lead internal and external quality analysts while presenting findings and recommendations to stakeholders.
• Collaborate with R&D and collection teams to refine project specifications and eliminate ambiguity in guidelines.

What we're looking for

• Bachelor's degree or equivalent practical experience.
• 4+ years of experience in ML data operations, data quality, or a comparable data-centric quality function.
• Working proficiency in Python for data manipulation and reporting.
• Hands-on experience using AI coding assistants to build working QA tools or analysis.
• Strong written and verbal communication skills.
• Experience designing labeling taxonomies or annotation guidelines and adjudicating ambiguous cases with vendors (preferred).
• Experience leading quality analysts and running human rating and evaluation programs (preferred).
• Familiarity with statistical quality methods, LLM/VLM prompt engineering, and building end-to-end QA tooling (preferred).