ML Data QA Lead

What you'll do

• Own the quality strategy and roadmap for the data request life cycle to detect anomalies and surface issues early.
• Translate ambiguous quality expectations into explicit, documented standards and decision rules for vendors and reviewers.
• Run quality assurance and control checks across pilot and production phases to validate trends before R&D consumption.
• Design and build new QA tools, review interfaces, and data pipelines using AI-assisted development.
• Introduce and iterate on model-assisted checks by measuring agreement against human labels.
• Verify analyses, metrics, and generated artifacts independently before publishing them to stakeholders.
• Lead internal and external quality analysts and present findings and recommendations to project teams and vendors.
• Partner with R&D teams to define project specifications and ensure guidelines are unambiguous for all users.

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 designing human rating and evaluation programs (preferred).
• Familiarity with statistical quality methods, including sampling strategy and inter-rater agreement (preferred).