Quality Analytics Lead - weloglobal

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

The Quality Analytics Lead is the dedicated technical resource bridging Welo Data’s Analytics and Quality organizations. Sitting within the Analytics team, this senior IC partners enterprise-wide with Quality Managers, Analysts, and leadership to design and maintain the data models, measurement frameworks, and analytical infrastructure that power evidence-based quality decisions across programs and regions.

At its core, this is an analytics engineering role. The primary responsibility is building and owning the quality data layer — the dbt models, data marts, and Python-driven modeling that transform raw operational data into a trusted, well-documented foundation the Quality organization can rely on. Experimentation, stakeholder consulting, and BI delivery are all extensions of that foundation, not parallel tracks.

The ideal candidate combines deep fluency in modern data modeling with a genuine understanding of quality operations, AI training data workflows, and experimental design. They ensure that the analytical systems they build directly improve how quality teams detect issues, validate improvements, and demonstrate impact to clients and leadership.