Perform quality evaluations of customer interactions using both manual and AI-assisted evaluation workflows.
Validate AI-generated QA scores and ensure alignment with defined quality frameworks.
Conduct targeted call and interaction scrubbing based on AI-identified risk signals.
Ensure adherence to SOPs, compliance requirements, and customer experience standards.
Leverage AI-based insights to prioritise interactions requiring QA intervention.
Focus QA effort on high-risk interactions instead of random sampling.
Escalate critical quality concerns and behavioural risks.
Analyze QA datasets to identify top failing parameters, agent behaviour patterns, customer experience gaps.
Work with structured datasets (Google Sheets/MS Excel) to generate actionable insights.
Support creation and maintenance of dashboards within QMS.
Provide feedback on feature enhancements related to AI models & functions in QMS.
Use QMS dashboards to monitor contact centre quality health, agent performance trends, experience metrics. (customer sentiment, interaction outcomes)
Take proactive action based on insights. (scrubbing, escalation, coaching recommendations)
Participate in QA calibration sessions to ensure scoring consistency.
Recommend improvements to scorecards, parameters, and evaluation frameworks.
Contribute to evolving QA processes toward AI-first quality assurance models.

1–4 years of experience in QA / Contact Center Quality Assurance.
Strong understanding of contact centre operations. (voice, chat, email)
Experience in QA monitoring, evaluation frameworks, and calibration processes.
Ability to identify behavioural patterns and quality risks.
Proficiency in Google Sheets/MS Excel (Formulas - ARRAYFORMULA, QUERY, etc.) for data structuring and transformation.
Experience with BI tools such as Looker Studio or similar is preferred.
Ability to interpret and analyze large datasets.
Understanding of performance metrics such as ratios (e.g. disconnect rate, short call rate) trends and anomalies.
Basic understanding of AI-assisted QA concepts such as automated call evaluation, sentiment analysis, pattern detection.
Ability to work with AI-generated outputs and validate their accuracy.
Openness to adapting to AI-driven workflows and automation.
Ability to provide clear, actionable feedback to agents, leads & operations.
Experience working with cross-functional teams. (QA, Operations, Product, Engineering)
Experience working with global teams located in different regions. (US, UK, Canada & India)
Strong verbal and written communication skills.
Language Proficiency - Must be C1 or above. (CEFR)
Exposure to data analysis & reports.

Quality Analyst

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