Quality Analyst - Primary Cybersource

Visa

2d•Hybrid

About The Position

The Quality Analyst is an individual contributor role responsible for advancing quality, risk, and control monitoring through a combination of manual evaluation, AI‑enabled quality intelligence, and data‑driven insight generation. The role supports first‑line‑of‑defense risk governance by performing quality reviews, monitoring control effectiveness, identifying emerging risks, and enabling continuous improvement across customer interactions and operational processes. The Quality Analyst analyzes patterns, identifies root causes, and influences operational change. The analyst partners closely with Client Care leadership, operations, training, and risk stakeholders to translate quality and control signals into actionable insights, remediation strategies, and performance improvements. This role leverages AI‑assisted quality tools, advanced analytics, and visualization platforms to surface non‑obvious insights, support governance of Hu & AI driven quality models, and deliver clear, decision‑ready narratives that inform leadership priorities and investments.

Requirements

2 or more years of work experience with a Bachelor’s Degree or an Advanced Degree (e.g. Masters, MBA, JD, MD, or PhD)
Experience performing quality assurance, testing, risk, or control monitoring activities in a customer service or operations environment.
Demonstrated ability to conduct manual quality reviews and support AI-assisted quality monitoring under moderate supervision.
Strong analytical and critical-thinking skills with the ability to identify trends, root causes, and performance gaps.
Proficiency with Microsoft Office tools (Excel, PowerPoint, Word) and experience navigating multiple systems and data sources.
Ability to clearly document findings, communicate insights, and collaborate effectively with cross-functional stakeholders.
Experience analyzing quality signals across both human-delivered and AI-delivered interactions.

Nice To Haves

3 or more years of work experience with a Bachelor’s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD)
Proficiency with data visualization and analytics tools (e.g., Power BI, Tableau) to build dashboards and performance reporting.
Experience translating automated quality outputs into prioritized coaching, remediation, or process improvement insights.
Familiarity with AI-enabled quality tools, speech/text analytics, and assisted scoring methodologies.

Responsibilities

Perform end‑to‑end quality reviews of customer interactions across voice, chat, email, case, and back‑office channels, ensuring accuracy, consistency, and compliance with documented standards.
Support human‑in‑the‑loop AI quality monitoring, including reviewing assisted and automated scoring outputs and providing structured feedback to improve model performance.
Identify quality defects, compliance risks, behavioral gaps, and control failures through manual and AI‑enabled analysis.
Analyze quality, risk, and control data across segments, channels, and time to identify trends, systemic issues, and emerging risk themes.
Translate quality and control findings into measurable business impact, connecting defects to process, policy, or behavioral drivers.
Compile, analyze, and deliver regular quality, risk, and performance reporting to Client Care leadership using dashboards and visual analytics.
Partner with Operations, Training, and Functional Leaders to reframe defects as process or policy gaps and co‑design targeted improvement actions.
Lead and participate in calibration sessions, governance forums, and quality consistency reviews to ensure alignment across teams, vendors, and AI models.
Serve as a trusted quality and risk advisor, influencing decisions, remediation priorities, and performance improvement strategies without direct people management.
Leverage AI tools (e.g., Copilot, assisted scoring, topic mining, speech and text analytics) to accelerate insight generation while validating AI outputs for accuracy and defensibility.
Identify systematic gaps in Auto/AI scoring and support governance efforts to improve model signal fidelity and exception handling.
Apply AI‑driven quality signals to prioritize high‑risk interactions and focus remediation efforts where impact is greatest.
Synthesize complex findings into clear, audience‑specific narratives that articulate what changed, why it matters, and what action is required.
Deliver concise insight briefs and executive‑ready materials that drive understanding, alignment, and action.