AI Data Excellence Manager

AI Data Excellence Manager

About this role: This is a high-performing opportunity to architect and uphold quality data annotation workflows so that AI models are built and evaluated on reliable data without compromising speed or efficiency.

The key objectives of this position include:

• Designing, refining, and documenting quality assurance processes and SOPs to ensure high-quality outputs.
• Establishing quality metrics (e.g., F1 score, inter-annotator agreement) that align with business objectives and industry standards.
• Continuously reviewing annotation workflows to identify risks and opportunities to increase efficiency and reduce errors.
• Acting as a subject matter expert on annotation quality, providing ongoing feedback, training, and support to annotators and project teams.

Key Responsibilities:

Strategic Leadership

• Developing and refining robust QA processes, SOPs, and statistical quality metrics to protect annotation integrity.
• Ensuring transparency and insight by creating dashboards and reports that reveal quality trends, root causes of errors, and improvement opportunities; communicating insights to leadership and clients.
• Championing tool innovation and efficiency by managing annotation and QA platforms and evaluating or implementing new automation tools to elevate efficiency and maintain quality.

Analysis & Reporting

• Leading in-depth data analysis to diagnose quality issues, assess strategy effectiveness, and uncover root causes of recurring errors.
• Developing dashboards for real-time quality metrics and project performance.
• Preparing and delivering strategic quality reports to senior management and clients, outlining quality trends, risks, and improvement plans.
• Collaborating with cross-functional teams to align on project goals and QA initiatives.

Operational Leadership

• Leading a team of Data Quality Analysts, providing mentorship and training to foster continuous improvement and accountability.
• Managing configuration and integration of annotation and quality control tools to ensure optimal performance.
• Identifying and implementing innovative quality control tools and automation technologies to streamline workflows and improve efficiency.

Required Qualifications:

• 3+ years in data quality management, data operations, or related AI/ML or data annotation roles.
• Proven experience designing and executing QA strategies for large-scale, multi-modal data annotation projects.
• Leadership experience managing remote or distributed teams.
• Deep understanding of data annotation processes, QA methodologies, and metrics (e.g., F1 score, inter-annotator agreement).
• Strong data-analysis skills and ability to translate findings into actionable recommendations.
• Excellent communication skills with experience presenting data and quality insights to technical and non-technical stakeholders.
• Proficiency with annotation and QA tools.
• High proficiency in Excel and Google Sheets; familiarity with Python and SQL.
• Familiarity with AI/ML pipelines concepts including data prep, model training, and evaluation.
• Ability to start ASAP or within 30 days.

Preferred Qualifications:

• Experience in agile or fast-paced tech environments with AI/ML pipelines.
• Experience in managed services or vendor-driven settings.
• Familiarity with prompt engineering and LLM-assisted workflows for annotation and validation.
• Knowledge of ethical AI practices and compliance frameworks.

Education / Certifications:

• Bachelor's degree in a technical field (e.g., Computer Science, Data Science) or equivalent professional experience.

Why you'll love this role:

• Remote-first flexibility.
• Opportunities for growth and professional development.
• Visible impact on the organization's success.
• Ambitious team culture.