Head of Data Quality

About the role

The Head of Data Quality at Snorkel AI is responsible for building and owning the quality system at the heart of the company's Data-as-a-Service (DaaS) business. The role involves designing the end-to-end quality architecture, ensuring quality is embedded throughout the DaaS lifecycle, and driving continuous improvement.

Responsibilities

• Establish Snorkel's quality strategy, standards, and operating model across contributors, datasets, and individual data points.
• Build processes, metrics, and governance mechanisms that enable quality to be measured, scaled, and continuously improved.
• Ensure quality is embedded throughout the DaaS lifecycle and reflected in commitments to customers.
• Define standards and operating mechanisms that drive high-quality outcomes across contributors, datasets, and engagements.
• Partner across Supply, Expert Contributor Experience, Product, and Engineering to operationalize and scale quality throughout the business.
• Leverage data, automation, and AI-assisted workflows to continuously improve quality, efficiency, and customer outcomes.
• Establish and scale the Quality function, starting as a hands-on builder and evolving the organization, operating model, and team as the business grows.
• Serve as a champion for Snorkel's quality approach with customers, prospects, and industry audiences, helping position quality as a key differentiator for the business.

Requirements

• 8+ years of experience in quality, operations, program management, or a related field, with a track record of building or significantly redesigning quality systems in complex, human-in-the-loop environments.
• Experience defining quality standards, measurement frameworks, and operating processes that drive consistent outcomes at scale.
• Strong analytical and problem-solving skills, including experience using data to measure performance, identify risks, and drive continuous improvement.
• Demonstrated ability to lead through influence and drive cross-functional initiatives across Product, Engineering, Operations, and business teams.
• Excellent communication and stakeholder management skills, with the ability to operate effectively from executive discussions through day-to-day execution.
• Comfortable operating in ambiguity, establishing structure where none exists, and building systems, processes, and teams from the ground up.
• Experience may come from AI data and evaluation organizations, or from other industries that operate complex quality systems at scale, including manufacturing, life sciences, localization, aerospace, or other highly customized production environments.
• Bonus points for experience building or scaling quality programs for AI data, model evaluation, or other human-in-the-loop AI workflows.
• Familiarity with AI-assisted quality methods, including LLM-as-judge, automated review systems, or other model-assisted approaches.
• Experience operating in environments with evolving requirements, complex task types, or rapidly changing definitions of quality.
• Master's degree in a relevant field, or an MBA with an operations or quality focus.

Qualifications

• Master's degree in a relevant field, or an MBA with an operations or quality focus.

Skills

• Strong analytical and problem-solving skills.
• Experience using data to measure performance, identify risks, and drive continuous improvement.
• Ability to lead through influence and drive cross-functional initiatives.
• Excellent communication and stakeholder management skills.
• Comfortable operating in ambiguity and building systems, processes, and teams from the ground up.
• Experience in AI data and evaluation organizations or other industries with complex quality systems.
• Familiarity with AI-assisted quality methods.

Benefits

Depending on your work location, the target annual salary base for this position can range from $280,000 to $350,000. All offers include equity compensation in the form of employee stock options.