Quality Analyst Reporting

Job Summary

As a Quality Analyst Reporting, you will be responsible for monitoring, evaluating, and improving the quality of Reporting interactions to ensure a high level of operational excellence. You will work closely with Quality Analyst, Data Reporting Managers, Subject Matter Experts (SMEs), and Operations teams to conduct quality audits, provide actionable feedback, identify improvement opportunities on evolving processes, tools, and ensure adherence to support processes, quality standards, and service level expectations. Act as a subject matter expert for quality processes, supporting audits, compliance reviews, and customer-facing quality initiatives. You will be responsible for creating Dashboards and reports of Quality checks.

Location: Pune, India

Division: Data Reporting

Employment Type: Full Time Permanent

Work Arrangement: On-site

Responsibilities

• Intelligent Risk-Based Auditing : Targeted Sampling: Shift away from traditional random 10% sampling to design and execute a risk-based sampling model driven by historical error metrics.

• Intelligent Risk-Based Auditing : Efficiency Optimization: Focus expert auditing resources exclusively on high-probability error transactions (e.g., complex multi-item checkouts, tenured anomalies, or edge cases flagged by AI).

• In-Line Guardrails Feedback Loops : Partner with the Technical Project Co-ordinator and Systems Engineering teams to design real-time feedback loops and automated validation gates, directly within core auditing tools (GRIME and ASGARD).

• Stop data discrepancies at the entry point, ensuring errors are flagged and corrected before they exit the primary audit phase.

• Data Quality Root Cause Analysis (RCA) : Systemic Issue Diagnostic: Conduct deep-dive Root Cause Analysis (RCA) on recurring database discrepancies to isolate underlying drivers (e.g., software tool lag, vague annotation guidelines, or ML model classification gaps).

• Guideline Optimization : Translate RCA findings into immediate corrective actions, updated documentation playbooks, and training huddles for the reporting floor.

• AI Model Guardrails Collaboration : Model Validation: Act as the authoritative validation layer for AI-driven engines, auditing and labeling the automated classification outputs to ensure a near-100% database gold standard.

• Mitigate Model Drift: Identify patterns where AI models begin to show bias or drift due to new cashier behaviors or store layouts, feeding structured corrections directly back to the Research and ML development teams.

  Candidate Profile Requirements

• Experience: 3+ years of experience in Quality Assurance, Quality Engineering, or Data Quality Control within high-volume data processing, analytics, or tech operations environments.

• Analytical Toolset: Strong proficiency in data analysis, including statistical tracking, Root Cause Analysis (RCA), and basic data tools (Excel, SQL, or dashboard reporting tools like BI).

• AI/ML Familiarity: Experience working alongside automated systems, data annotation platforms, or ML pre-labeling loops is highly preferred.

• Problem-Solving Mindset: A proactive, 'build-the-guardrail' attitude someone who seeks to fix the process rather than just report the error.

• Communication: Exceptional collaborative skills, with the ability to bridge communication between front-line annotators, technical project coordinators, and ML engineers.

• Education: Graduation in Engineering background

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.