Owns client-specific quality programs, performance metrics, and escalations while leading team development and cross-functional coordination.
Shared accountability for client outcomes and team performance with the Ops Manager.
Set client QA strategies (sampling design, audit methods, acceptance thresholds) and adapt to scope/volume changes.
Run root-cause analyses; drive CAPA plans with owners, timelines, and effectiveness checks.
Plan training & certification for raters/annotators and coordinators; track completion and impact.
Maintain dashboards (throughput, accuracy, productivity, cost) and convert insights into actions.
Manage client escalations; present options, trade-offs, and recovery paths.
Standardize SOPs, templates, and checklists; remove bottlenecks.
Pilot small automations (macros, templates, RPA/API handoffs) with Ops Tech; scale wins.
Coach P1s and C2–C3 on tools, workflows, and QA craft.
Ensure compliance/security across data handling and platform access.

Bachelor’s degree or equivalent experience in Business, Operations, Quality, or Data/Engineering.
2+ years in quality/ops with hands-on QA and workforce/training coordination.
1+ years leading people/pods (formal or informal).
Multi-project planning and stakeholder management.
Clear client communications and governance cadence participation.
Strong spreadsheets, PM/task boards, and basic BI; ETL familiarity is a plus.
Capacity planning with vendors; confident escalation/negotiation.
Effective in global, distributed teams.
Near-native English with strong writing and editorial skills.
Hands-on with generative AI tools (text/voice/video).
Background in QA testing, rubric design, or AI safety/ethics evaluation.
Familiarity with data-annotation platforms and model-evaluation tools.
Ability to interpret code/datasets/workflows at a conceptual level (no coding required).

Quality Analyst, Gen AI Data Quality

Key skills