Senior Manager, Data Quality & Evaluation

About Argos MultilingualArgos Multilingual is a global language, data, and AI services company helping leading organizations build, evaluate, and improve AI systems.Our Data Services team partners with AI labs, technology companies, and enterprise AI teams on complex human data programs across multilingual evaluation, speech and audio, model response evaluation, expert review, annotation, and emerging agentic workflows.As AI systems become more capable and more complex, high-quality human evaluation is becoming a critical part of how models are trained, tested, and improved. We are building a Data Services organization focused on quality, scalability, operational excellence, and customer trust.Summary of the roleThe Senior Manager, Data Quality to helps build the quality engine behind Argos’ AI Data Services business.In this role, you will define how we evaluate, calibrate, measure, and scale high-quality human data programs for leading AI companies. You will design quality frameworks for data collection, annotation, evaluation, review, calibration, adjudication, and customer reporting.You will collaborate closely with Program Management, Supply Chain, Solutions, Sales, and customer-facing teams to turn customer requirements into clear, scalable quality workflows. You will help ensure that every program has the right evaluation methodology, task instructions, reviewer calibration, sampling approach, escalation process, and performance reporting in place.This is a high-impact role for someone who enjoys building systems, improving quality, working cross-functionally, and operating in a fast-moving AI services environment.ResponsibilitiesBuild quality systems for AI data programsDesign and manage quality frameworks for AI data and evaluation programs.Translate customer requirements into clear quality standards, rubrics, acceptance criteria, review processes, and KPIs.Build quality workflows that are practical, scalable, and trusted by customers as programs move from pilot to production.Identify quality risks early and work with delivery teams to resolve issues before they impact timelines, customer confidence, or program outcomes.Create repeatable quality processes across calibration, QA sampling, adjudication, reviewer performance tracking, and customer reporting.Lead evaluation, calibration, and QA processesSupport quality operations across multilingual evaluation, speech/audio QA, transcription, data annotation, human preference evaluation, expert review, model response evaluation, coding evaluation, tool-use evaluation, and agent workflow evaluation.Create and improve rubrics, task instructions, reviewer guides, calibration exercises, golden datasets, and quality reporting templates.Lead calibration sessions with reviewers, annotators, quality specialists, delivery teams, and customer stakeholders.Define quality thresholds, error taxonomies, escalation rules, and corrective action plans.Monitor reviewer agreement, disagreement trends, error rates, contributor performance, and root causes of quality variance.Turn QA findings into practical improvements to instructions, training, tooling, staffing, and delivery workflows.Partner with customers and internal teamsAct as a quality lead for strategic customer programs when needed.Support customer-facing quality readouts, pilot retrospectives, business reviews, escalations, and scale-up discussions.Provide clear, data-backed reporting that explains quality performance, risks, corrective actions, and next steps.Partner with Program Management, Supply Chain, Solutions, Sales, and Operations to ensure programs are set up for quality success from the start.Work with Supply Chain to define reviewer profiles, evaluator requirements, language requirements, domain expertise, onboarding needs, and performance expectations.Help determine when programs require expert reviewers, QA leads, language leads, technical reviewers, or specialized evaluation talent.Build and develop the quality functionBuild reusable quality assets such as calibration packs, QA reports, rubric libraries, error taxonomies, scorecards, and sample evaluation frameworks.Identify repeatable patterns across programs and turn them into standardized approaches that help the business scale.Improve visibility into quality performance across programs, reviewers, contributors, and workflows.Manage, coach, and support Quality Managers, Quality Leads, Quality Specialists, reviewers, or QA contributors assigned to Data Services programs.Coach team members on quality judgment, customer communication, escalation handling, reporting, and root-cause analysis.Identify hiring, training, and coverage needs as the Data Services business grows.Create a culture of quality ownership, accountability, and continuous improvement.People managementAnticipate and communicate the needs identified for the team under your responsibility.Train, and support team members, advocate for upskilling and promote career growth.Be responsible