Quality Analyst — Generative AI Data Quality

About Welo Data Welo Data, a Welo Global brand, is the multilingual data and evaluation partner for foundation labs and enterprises deploying GenAI systems globally. They deliver the human judgment, data infrastructure, and evaluation systems that ensure AI models perform reliably across languages, cultures, and real-world contexts, at every stage from training through deployment.  Its global network of 500,000+ vetted experts spans 300+ languages and locales, enabling high-quality multilingual data creation and structured model evaluation across the full spectrum of modern AI applications — from large language models and voice and speech systems to agentic workflows and robotics and embodied AI. This breadth of linguistic, cultural, and domain expertise enables Welo Data to address critical AI development challenges, including safety, bias, inclusivity, and cross-lingual reliability. A unified global operating model, led by specialized program and quality experts and grounded in assessment-driven talent selection, localized rubrics, and continuous calibration, ensures consistent performance across languages, domains, and modalities. Underpinning all of this is NIMO™ (Network Identity Management and Operations), Welo Data's proprietary identity and fraud-prevention framework. Built to maintain data integrity and workforce trust across a global contributor base, NIMO combines advanced verification, continuous monitoring, and structured QA to ensure every dataset is accurate, traceable, and culturally grounded. welodata.ai    Role Purpose 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   Key Responsibilities ·       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.