Quality Analyst, Gen AI Data Quality

This is a fully remote position, open to applicants in Asia.

📋 Description

• Responsible for client-specific quality initiatives, performance metrics, and managing escalations while facilitating team growth and cross-departmental collaboration.

• Develop and implement client QA strategies including sampling design, audit methodologies, and acceptance criteria, while adjusting to changes in scope and volume.

• Conduct root-cause analyses and lead CAPA initiatives with designated owners, timelines, and assessments of effectiveness.

• Organize training and certification programs for raters, annotators, and coordinators, monitoring completion rates and their impact.

• Maintain performance dashboards (throughput, accuracy, productivity, cost) and translate insights into actionable strategies.

• Handle client escalations by presenting viable options, trade-offs, and recovery strategies.

• Standardize SOPs, templates, and checklists to eliminate bottlenecks.

• Test small automation projects (macros, templates, RPA/API integrations) with Ops Tech; scale successful outcomes.

• Mentor P1s and C2–C3 on tools, workflows, and quality assurance practices.

• Ensure compliance and security in data handling and platform access.

⛳️ Requirements

• Bachelor’s degree or equivalent experience in Business, Operations, Quality, or Data/Engineering.

• Over 2 years of experience in quality/operations with direct involvement in QA and workforce/training management.

• At least 1 year of experience in a leadership role, whether formal or informal.

• Proficient in multi-project planning and managing stakeholder relationships.

• Strong skills in spreadsheets, project management/task boards, and basic business intelligence; familiarity with ETL processes is advantageous.

• Effective in working with global, distributed teams.

• Near-native English proficiency with excellent writing and editorial abilities.

• Practical experience with generative AI tools (text, voice, video).

• Background in QA testing, rubric development, or AI safety/ethics evaluation.

• Knowledge of data-annotation platforms and model-evaluation tools.

• Capability to understand code, datasets, and workflows at a conceptual level (coding skills are not required).

🏝️ Benefits

• Flexible work arrangements

• Professional development opportunities