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Niche ML-data QA skillset but mid-level experience increases applicant density.
Specialized ML/data QA and governance requirements limit cross-industry transferability.
Explicit five-year requirement plus many mandatory ML QA, MLOps, and tooling skills.
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Design and implement data validation and QA/QC solutions to ensure high data integrity across AI/ML datasets and pipelines.
Lead testing of data ingestion, transformation, AI model outputs, and validation of data governance, privacy, and regulatory compliance.
Develop automated testing frameworks integrated within CI/CD and MLOps workflows; communicate quality status, risks, and corrective actions to stakeholders.
Bachelor's degree or equivalent directly related experience.
Minimum 5 years of related work experience in data quality management and QA/QC for AI/ML.
Strong programming skills in Python, C#, or R for automation and data analysis.
Experience with automated testing frameworks (PyTest), data validation tools (Great Expectations), and CI/CD integration in MLOps.
Experienced in developing complex automated testing solutions for large, ML-ready datasets and AI model validation within AEC or similar domains.
Able to independently own projects while collaborating in cross-functional teams, offering expert guidance on data quality and AI governance.
Familiar with end-to-end data quality lifecycle including bias detection, model drift tracking, defect root cause analysis, and continuous improvement initiatives.